Merge branch 'master' into ops-changes

README.md

@@ -119,6 +119,9 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang
 
 1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)**
    - Releases a new stable version (e.g., v0.7.0) roughly every week.
+   - Starting from v0.4.0 patch versions will be used for fixes backported onto the current stable release.
+   - Minor versions will be used for releases off the master branch.
+   - Patch versions may still be used for releases on the master branch in cases where a backport would not make sense.
    - Commits outside of the stable release tags may be very unstable and break many custom nodes.
    - Serves as the foundation for the desktop release
 
@@ -209,6 +212,8 @@ Python 3.14 works but you may encounter issues with the torch compile node. The
 
 Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
 
+torch 2.4 and above is supported but some features might only work on newer versions. We generally recommend using the latest major version of pytorch unless it is less than 2 weeks old.
+
 ### Instructions:
 
 Git clone this repo.

app/model_manager.py

@@ -44,7 +44,7 @@ class ModelFileManager:
         @routes.get("/experiment/models/{folder}")
         async def get_all_models(request):
             folder = request.match_info.get("folder", None)
-            if not folder in folder_paths.folder_names_and_paths:
+            if folder not in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
             files = self.get_model_file_list(folder)
             return web.json_response(files)
@@ -55,7 +55,7 @@ class ModelFileManager:
             path_index = int(request.match_info.get("path_index", None))
             filename = request.match_info.get("filename", None)
 
-            if not folder_name in folder_paths.folder_names_and_paths:
+            if folder_name not in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
 
             folders = folder_paths.folder_names_and_paths[folder_name]

comfy/clip_model.py

@@ -2,6 +2,25 @@ import torch
 from comfy.ldm.modules.attention import optimized_attention_for_device
 import comfy.ops
 
+def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
+    image = image[:, :, :, :3] if image.shape[3] > 3 else image
+    mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
+    std = torch.tensor(std, device=image.device, dtype=image.dtype)
+    image = image.movedim(-1, 1)
+    if not (image.shape[2] == size and image.shape[3] == size):
+        if crop:
+            scale = (size / min(image.shape[2], image.shape[3]))
+            scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
+        else:
+            scale_size = (size, size)
+
+        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
+        h = (image.shape[2] - size)//2
+        w = (image.shape[3] - size)//2
+        image = image[:,:,h:h+size,w:w+size]
+    image = torch.clip((255. * image), 0, 255).round() / 255.0
+    return (image - mean.view([3,1,1])) / std.view([3,1,1])
+
 class CLIPAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):
         super().__init__()

comfy/clip_vision.py

@@ -1,6 +1,5 @@
 from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace
 import os
-import torch
 import json
 import logging
 
@@ -17,24 +16,7 @@ class Output:
     def __setitem__(self, key, item):
         setattr(self, key, item)
 
-def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
+clip_preprocess = comfy.clip_model.clip_preprocess  # Prevent some stuff from breaking, TODO: remove eventually
-    image = image[:, :, :, :3] if image.shape[3] > 3 else image
-    mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
-    std = torch.tensor(std, device=image.device, dtype=image.dtype)
-    image = image.movedim(-1, 1)
-    if not (image.shape[2] == size and image.shape[3] == size):
-        if crop:
-            scale = (size / min(image.shape[2], image.shape[3]))
-            scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
-        else:
-            scale_size = (size, size)
-
-        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
-        h = (image.shape[2] - size)//2
-        w = (image.shape[3] - size)//2
-        image = image[:,:,h:h+size,w:w+size]
-    image = torch.clip((255. * image), 0, 255).round() / 255.0
-    return (image - mean.view([3,1,1])) / std.view([3,1,1])
 
 IMAGE_ENCODERS = {
     "clip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
@@ -73,7 +55,7 @@ class ClipVisionModel():
 
     def encode_image(self, image, crop=True):
         comfy.model_management.load_model_gpu(self.patcher)
-        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
+        pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
         out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2)
 
         outputs = Output()

comfy/context_windows.py

@@ -143,7 +143,7 @@ class IndexListContextHandler(ContextHandlerABC):
         # if multiple conds, split based on primary region
         if self.split_conds_to_windows and len(cond_in) > 1:
             region = window.get_region_index(len(cond_in))
-            logging.info(f"Splitting conds to windows; using region {region} for window {window[0]}-{window[-1]} with center ratio {window.center_ratio:.3f}")
+            logging.info(f"Splitting conds to windows; using region {region} for window {window.index_list[0]}-{window.index_list[-1]} with center ratio {window.center_ratio:.3f}")
             cond_in = [cond_in[region]]
         # cond object is a list containing a dict - outer list is irrelevant, so just loop through it
         for actual_cond in cond_in:
@@ -188,6 +188,12 @@ class IndexListContextHandler(ContextHandlerABC):
                                 audio_cond = cond_value.cond
                                 if audio_cond.ndim > 1 and audio_cond.size(1) == x_in.size(self.dim):
                                     new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(audio_cond, device, dim=1))
+                            # Handle vace_context (temporal dim is 3)
+                            elif cond_key == "vace_context" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
+                                vace_cond = cond_value.cond
+                                if vace_cond.ndim >= 4 and vace_cond.size(3) == x_in.size(self.dim):
+                                    sliced_vace = window.get_tensor(vace_cond, device, dim=3, retain_index_list=self.cond_retain_index_list)
+                                    new_cond_item[cond_key] = cond_value._copy_with(sliced_vace)
                             # if has cond that is a Tensor, check if needs to be subset
                             elif hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
                                 if  (self.dim < cond_value.cond.ndim and cond_value.cond.size(self.dim) == x_in.size(self.dim)) or \

comfy/hooks.py

@@ -527,7 +527,8 @@ class HookKeyframeGroup:
                         if self._current_keyframe.get_effective_guarantee_steps(max_sigma) > 0:
                             break
                     # if eval_c is outside the percent range, stop looking further
-                    else: break
+                    else:
+                        break
         # update steps current context is used
         self._current_used_steps += 1
         # update current timestep this was performed on

comfy/k_diffusion/sampling.py

@@ -74,6 +74,9 @@ def get_ancestral_step(sigma_from, sigma_to, eta=1.):
 
 def default_noise_sampler(x, seed=None):
     if seed is not None:
+        if x.device == torch.device("cpu"):
+            seed += 1
+
         generator = torch.Generator(device=x.device)
         generator.manual_seed(seed)
     else:

comfy/latent_formats.py

@@ -407,6 +407,9 @@ class LTXV(LatentFormat):
 
         self.latent_rgb_factors_bias = [-0.0571, -0.1657, -0.2512]
 
+class LTXAV(LTXV):
+    pass
+
 class HunyuanVideo(LatentFormat):
     latent_channels = 16
     latent_dimensions = 3

comfy/ldm/chroma_radiance/model.py

@@ -270,7 +270,7 @@ class ChromaRadiance(Chroma):
         bad_keys = tuple(
             k
             for k, v in overrides.items()
-            if type(v) != type(getattr(params, k)) and (v is not None or k not in nullable_keys)
+            if not isinstance(v, type(getattr(params, k))) and (v is not None or k not in nullable_keys)
         )
         if bad_keys:
             e = f"Invalid value(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}"

comfy/ldm/hunyuan_video/upsampler.py

@@ -3,7 +3,8 @@ import torch.nn as nn
 import torch.nn.functional as F
 from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, VideoConv3d
 from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm
-import model_management, model_patcher
+import model_management
+import model_patcher
 
 class SRResidualCausalBlock3D(nn.Module):
     def __init__(self, channels: int):

comfy/ldm/lightricks/av_model.py

@@ -0,0 +1,837 @@
+from typing import Tuple
+import torch
+import torch.nn as nn
+from comfy.ldm.lightricks.model import (
+    CrossAttention,
+    FeedForward,
+    AdaLayerNormSingle,
+    PixArtAlphaTextProjection,
+    LTXVModel,
+)
+from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier
+import comfy.ldm.common_dit
+
+class BasicAVTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        v_dim,
+        a_dim,
+        v_heads,
+        a_heads,
+        vd_head,
+        ad_head,
+        v_context_dim=None,
+        a_context_dim=None,
+        attn_precision=None,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+
+        self.attn_precision = attn_precision
+
+        self.attn1 = CrossAttention(
+            query_dim=v_dim,
+            heads=v_heads,
+            dim_head=vd_head,
+            context_dim=None,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+        self.audio_attn1 = CrossAttention(
+            query_dim=a_dim,
+            heads=a_heads,
+            dim_head=ad_head,
+            context_dim=None,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        self.attn2 = CrossAttention(
+            query_dim=v_dim,
+            context_dim=v_context_dim,
+            heads=v_heads,
+            dim_head=vd_head,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+        self.audio_attn2 = CrossAttention(
+            query_dim=a_dim,
+            context_dim=a_context_dim,
+            heads=a_heads,
+            dim_head=ad_head,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        # Q: Video, K,V: Audio
+        self.audio_to_video_attn = CrossAttention(
+            query_dim=v_dim,
+            context_dim=a_dim,
+            heads=a_heads,
+            dim_head=ad_head,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        # Q: Audio, K,V: Video
+        self.video_to_audio_attn = CrossAttention(
+            query_dim=a_dim,
+            context_dim=v_dim,
+            heads=a_heads,
+            dim_head=ad_head,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        self.ff = FeedForward(
+            v_dim, dim_out=v_dim, glu=True, dtype=dtype, device=device, operations=operations
+        )
+        self.audio_ff = FeedForward(
+            a_dim, dim_out=a_dim, glu=True, dtype=dtype, device=device, operations=operations
+        )
+
+        self.scale_shift_table = nn.Parameter(torch.empty(6, v_dim, device=device, dtype=dtype))
+        self.audio_scale_shift_table = nn.Parameter(
+            torch.empty(6, a_dim, device=device, dtype=dtype)
+        )
+
+        self.scale_shift_table_a2v_ca_audio = nn.Parameter(
+            torch.empty(5, a_dim, device=device, dtype=dtype)
+        )
+        self.scale_shift_table_a2v_ca_video = nn.Parameter(
+            torch.empty(5, v_dim, device=device, dtype=dtype)
+        )
+
+    def get_ada_values(
+        self, scale_shift_table: torch.Tensor, batch_size: int, timestep: torch.Tensor, indices: slice = slice(None, None)
+    ):
+        num_ada_params = scale_shift_table.shape[0]
+
+        ada_values = (
+            scale_shift_table[indices].unsqueeze(0).unsqueeze(0).to(device=timestep.device, dtype=timestep.dtype)
+            + timestep.reshape(batch_size, timestep.shape[1], num_ada_params, -1)[:, :, indices, :]
+        ).unbind(dim=2)
+        return ada_values
+
+    def get_av_ca_ada_values(
+        self,
+        scale_shift_table: torch.Tensor,
+        batch_size: int,
+        scale_shift_timestep: torch.Tensor,
+        gate_timestep: torch.Tensor,
+        num_scale_shift_values: int = 4,
+    ):
+        scale_shift_ada_values = self.get_ada_values(
+            scale_shift_table[:num_scale_shift_values, :],
+            batch_size,
+            scale_shift_timestep,
+        )
+        gate_ada_values = self.get_ada_values(
+            scale_shift_table[num_scale_shift_values:, :],
+            batch_size,
+            gate_timestep,
+        )
+
+        scale_shift_chunks = [t.squeeze(2) for t in scale_shift_ada_values]
+        gate_ada_values = [t.squeeze(2) for t in gate_ada_values]
+
+        return (*scale_shift_chunks, *gate_ada_values)
+
+    def forward(
+        self,
+        x: Tuple[torch.Tensor, torch.Tensor],
+        v_context=None,
+        a_context=None,
+        attention_mask=None,
+        v_timestep=None,
+        a_timestep=None,
+        v_pe=None,
+        a_pe=None,
+        v_cross_pe=None,
+        a_cross_pe=None,
+        v_cross_scale_shift_timestep=None,
+        a_cross_scale_shift_timestep=None,
+        v_cross_gate_timestep=None,
+        a_cross_gate_timestep=None,
+        transformer_options=None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        run_vx = transformer_options.get("run_vx", True)
+        run_ax = transformer_options.get("run_ax", True)
+
+        vx, ax = x
+        run_ax = run_ax and ax.numel() > 0
+        run_a2v = run_vx and transformer_options.get("a2v_cross_attn", True) and ax.numel() > 0
+        run_v2a = run_ax and transformer_options.get("v2a_cross_attn", True)
+
+        if run_vx:
+            vshift_msa, vscale_msa, vgate_msa = (
+                self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(0, 3))
+            )
+
+            norm_vx = comfy.ldm.common_dit.rms_norm(vx) * (1 + vscale_msa) + vshift_msa
+            vx += self.attn1(norm_vx, pe=v_pe, transformer_options=transformer_options) * vgate_msa
+            vx += self.attn2(
+                comfy.ldm.common_dit.rms_norm(vx),
+                context=v_context,
+                mask=attention_mask,
+                transformer_options=transformer_options,
+            )
+
+            del vshift_msa, vscale_msa, vgate_msa
+
+        if run_ax:
+            ashift_msa, ascale_msa, agate_msa = (
+                self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(0, 3))
+            )
+
+            norm_ax = comfy.ldm.common_dit.rms_norm(ax) * (1 + ascale_msa) + ashift_msa
+            ax += (
+                self.audio_attn1(norm_ax, pe=a_pe, transformer_options=transformer_options)
+                * agate_msa
+            )
+            ax += self.audio_attn2(
+                comfy.ldm.common_dit.rms_norm(ax),
+                context=a_context,
+                mask=attention_mask,
+                transformer_options=transformer_options,
+            )
+
+            del ashift_msa, ascale_msa, agate_msa
+
+        # Audio - Video cross attention.
+        if run_a2v or run_v2a:
+            # norm3
+            vx_norm3 = comfy.ldm.common_dit.rms_norm(vx)
+            ax_norm3 = comfy.ldm.common_dit.rms_norm(ax)
+
+            (
+                scale_ca_audio_hidden_states_a2v,
+                shift_ca_audio_hidden_states_a2v,
+                scale_ca_audio_hidden_states_v2a,
+                shift_ca_audio_hidden_states_v2a,
+                gate_out_v2a,
+            ) = self.get_av_ca_ada_values(
+                self.scale_shift_table_a2v_ca_audio,
+                ax.shape[0],
+                a_cross_scale_shift_timestep,
+                a_cross_gate_timestep,
+            )
+
+            (
+                scale_ca_video_hidden_states_a2v,
+                shift_ca_video_hidden_states_a2v,
+                scale_ca_video_hidden_states_v2a,
+                shift_ca_video_hidden_states_v2a,
+                gate_out_a2v,
+            ) = self.get_av_ca_ada_values(
+                self.scale_shift_table_a2v_ca_video,
+                vx.shape[0],
+                v_cross_scale_shift_timestep,
+                v_cross_gate_timestep,
+            )
+
+            if run_a2v:
+                vx_scaled = (
+                    vx_norm3 * (1 + scale_ca_video_hidden_states_a2v)
+                    + shift_ca_video_hidden_states_a2v
+                )
+                ax_scaled = (
+                    ax_norm3 * (1 + scale_ca_audio_hidden_states_a2v)
+                    + shift_ca_audio_hidden_states_a2v
+                )
+                vx += (
+                    self.audio_to_video_attn(
+                        vx_scaled,
+                        context=ax_scaled,
+                        pe=v_cross_pe,
+                        k_pe=a_cross_pe,
+                        transformer_options=transformer_options,
+                    )
+                    * gate_out_a2v
+                )
+
+                del gate_out_a2v
+                del scale_ca_video_hidden_states_a2v,\
+                    shift_ca_video_hidden_states_a2v,\
+                    scale_ca_audio_hidden_states_a2v,\
+                    shift_ca_audio_hidden_states_a2v,\
+
+            if run_v2a:
+                ax_scaled = (
+                    ax_norm3 * (1 + scale_ca_audio_hidden_states_v2a)
+                    + shift_ca_audio_hidden_states_v2a
+                )
+                vx_scaled = (
+                    vx_norm3 * (1 + scale_ca_video_hidden_states_v2a)
+                    + shift_ca_video_hidden_states_v2a
+                )
+                ax += (
+                    self.video_to_audio_attn(
+                        ax_scaled,
+                        context=vx_scaled,
+                        pe=a_cross_pe,
+                        k_pe=v_cross_pe,
+                        transformer_options=transformer_options,
+                    )
+                    * gate_out_v2a
+                )
+
+                del gate_out_v2a
+                del scale_ca_video_hidden_states_v2a,\
+                    shift_ca_video_hidden_states_v2a,\
+                    scale_ca_audio_hidden_states_v2a,\
+                    shift_ca_audio_hidden_states_v2a
+
+        if run_vx:
+            vshift_mlp, vscale_mlp, vgate_mlp = (
+                self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(3, None))
+            )
+
+            vx_scaled = comfy.ldm.common_dit.rms_norm(vx) * (1 + vscale_mlp) + vshift_mlp
+            vx += self.ff(vx_scaled) * vgate_mlp
+            del vshift_mlp, vscale_mlp, vgate_mlp
+
+        if run_ax:
+            ashift_mlp, ascale_mlp, agate_mlp = (
+                self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(3, None))
+            )
+
+            ax_scaled = comfy.ldm.common_dit.rms_norm(ax) * (1 + ascale_mlp) + ashift_mlp
+            ax += self.audio_ff(ax_scaled) * agate_mlp
+
+            del ashift_mlp, ascale_mlp, agate_mlp
+
+
+        return vx, ax
+
+
+class LTXAVModel(LTXVModel):
+    """LTXAV model for audio-video generation."""
+
+    def __init__(
+        self,
+        in_channels=128,
+        audio_in_channels=128,
+        cross_attention_dim=4096,
+        audio_cross_attention_dim=2048,
+        attention_head_dim=128,
+        audio_attention_head_dim=64,
+        num_attention_heads=32,
+        audio_num_attention_heads=32,
+        caption_channels=3840,
+        num_layers=48,
+        positional_embedding_theta=10000.0,
+        positional_embedding_max_pos=[20, 2048, 2048],
+        audio_positional_embedding_max_pos=[20],
+        causal_temporal_positioning=False,
+        vae_scale_factors=(8, 32, 32),
+        use_middle_indices_grid=False,
+        timestep_scale_multiplier=1000.0,
+        av_ca_timestep_scale_multiplier=1.0,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs,
+    ):
+        # Store audio-specific parameters
+        self.audio_in_channels = audio_in_channels
+        self.audio_cross_attention_dim = audio_cross_attention_dim
+        self.audio_attention_head_dim = audio_attention_head_dim
+        self.audio_num_attention_heads = audio_num_attention_heads
+        self.audio_positional_embedding_max_pos = audio_positional_embedding_max_pos
+
+        # Calculate audio dimensions
+        self.audio_inner_dim = audio_num_attention_heads * audio_attention_head_dim
+        self.audio_out_channels = audio_in_channels
+
+        # Audio-specific constants
+        self.num_audio_channels = 8
+        self.audio_frequency_bins = 16
+
+        self.av_ca_timestep_scale_multiplier = av_ca_timestep_scale_multiplier
+
+        super().__init__(
+            in_channels=in_channels,
+            cross_attention_dim=cross_attention_dim,
+            attention_head_dim=attention_head_dim,
+            num_attention_heads=num_attention_heads,
+            caption_channels=caption_channels,
+            num_layers=num_layers,
+            positional_embedding_theta=positional_embedding_theta,
+            positional_embedding_max_pos=positional_embedding_max_pos,
+            causal_temporal_positioning=causal_temporal_positioning,
+            vae_scale_factors=vae_scale_factors,
+            use_middle_indices_grid=use_middle_indices_grid,
+            timestep_scale_multiplier=timestep_scale_multiplier,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+            **kwargs,
+        )
+
+    def _init_model_components(self, device, dtype, **kwargs):
+        """Initialize LTXAV-specific components."""
+        # Audio-specific projections
+        self.audio_patchify_proj = self.operations.Linear(
+            self.audio_in_channels, self.audio_inner_dim, bias=True, dtype=dtype, device=device
+        )
+
+        # Audio-specific AdaLN
+        self.audio_adaln_single = AdaLayerNormSingle(
+            self.audio_inner_dim,
+            use_additional_conditions=False,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+
+        num_scale_shift_values = 4
+        self.av_ca_video_scale_shift_adaln_single = AdaLayerNormSingle(
+            self.inner_dim,
+            use_additional_conditions=False,
+            embedding_coefficient=num_scale_shift_values,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+        self.av_ca_a2v_gate_adaln_single = AdaLayerNormSingle(
+            self.inner_dim,
+            use_additional_conditions=False,
+            embedding_coefficient=1,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+        self.av_ca_audio_scale_shift_adaln_single = AdaLayerNormSingle(
+            self.audio_inner_dim,
+            use_additional_conditions=False,
+            embedding_coefficient=num_scale_shift_values,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+        self.av_ca_v2a_gate_adaln_single = AdaLayerNormSingle(
+            self.audio_inner_dim,
+            use_additional_conditions=False,
+            embedding_coefficient=1,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+
+        # Audio caption projection
+        self.audio_caption_projection = PixArtAlphaTextProjection(
+            in_features=self.caption_channels,
+            hidden_size=self.audio_inner_dim,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
+        )
+
+    def _init_transformer_blocks(self, device, dtype, **kwargs):
+        """Initialize transformer blocks for LTXAV."""
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicAVTransformerBlock(
+                    v_dim=self.inner_dim,
+                    a_dim=self.audio_inner_dim,
+                    v_heads=self.num_attention_heads,
+                    a_heads=self.audio_num_attention_heads,
+                    vd_head=self.attention_head_dim,
+                    ad_head=self.audio_attention_head_dim,
+                    v_context_dim=self.cross_attention_dim,
+                    a_context_dim=self.audio_cross_attention_dim,
+                    dtype=dtype,
+                    device=device,
+                    operations=self.operations,
+                )
+                for _ in range(self.num_layers)
+            ]
+        )
+
+    def _init_output_components(self, device, dtype):
+        """Initialize output components for LTXAV."""
+        # Video output components
+        super()._init_output_components(device, dtype)
+        # Audio output components
+        self.audio_scale_shift_table = nn.Parameter(
+            torch.empty(2, self.audio_inner_dim, dtype=dtype, device=device)
+        )
+        self.audio_norm_out = self.operations.LayerNorm(
+            self.audio_inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device
+        )
+        self.audio_proj_out = self.operations.Linear(
+            self.audio_inner_dim, self.audio_out_channels, dtype=dtype, device=device
+        )
+        self.a_patchifier = AudioPatchifier(1, start_end=True)
+
+    def separate_audio_and_video_latents(self, x, audio_length):
+        """Separate audio and video latents from combined input."""
+        # vx = x[:, : self.in_channels]
+        # ax = x[:, self.in_channels :]
+        #
+        # ax = ax.reshape(ax.shape[0], -1)
+        # ax = ax[:, : audio_length * self.num_audio_channels * self.audio_frequency_bins]
+        #
+        # ax = ax.reshape(
+        #     ax.shape[0], self.num_audio_channels, audio_length, self.audio_frequency_bins
+        # )
+
+        vx = x[0]
+        ax = x[1] if len(x) > 1 else torch.zeros(
+            (vx.shape[0], self.num_audio_channels, 0, self.audio_frequency_bins),
+            device=vx.device, dtype=vx.dtype
+        )
+        return vx, ax
+
+    def recombine_audio_and_video_latents(self, vx, ax, target_shape=None):
+        if ax.numel() == 0:
+            return vx
+        else:
+            return [vx, ax]
+        """Recombine audio and video latents for output."""
+        # if ax.device != vx.device or ax.dtype != vx.dtype:
+        #     logging.warning("Audio and video latents are on different devices or dtypes.")
+        #     ax = ax.to(device=vx.device, dtype=vx.dtype)
+        #     logging.warning(f"Audio audio latent moved to device: {ax.device}, dtype: {ax.dtype}")
+        #
+        # ax = ax.reshape(ax.shape[0], -1)
+        # # pad to f x h x w of the video latents
+        # divisor = vx.shape[-1] * vx.shape[-2] * vx.shape[-3]
+        # if target_shape is None:
+        #     repetitions = math.ceil(ax.shape[-1] / divisor)
+        # else:
+        #     repetitions = target_shape[1] - vx.shape[1]
+        # padded_len = repetitions * divisor
+        # ax = F.pad(ax, (0, padded_len - ax.shape[-1]))
+        # ax = ax.reshape(ax.shape[0], -1, vx.shape[-3], vx.shape[-2], vx.shape[-1])
+        # return torch.cat([vx, ax], dim=1)
+
+    def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
+        """Process input for LTXAV - separate audio and video, then patchify."""
+        audio_length = kwargs.get("audio_length", 0)
+        # Separate audio and video latents
+        vx, ax = self.separate_audio_and_video_latents(x, audio_length)
+        [vx, v_pixel_coords, additional_args] = super()._process_input(
+            vx, keyframe_idxs, denoise_mask, **kwargs
+        )
+
+        ax, a_latent_coords = self.a_patchifier.patchify(ax)
+        ax = self.audio_patchify_proj(ax)
+
+        # additional_args.update({"av_orig_shape": list(x.shape)})
+        return [vx, ax], [v_pixel_coords, a_latent_coords], additional_args
+
+    def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwargs):
+        """Prepare timestep embeddings."""
+        # TODO: some code reuse is needed here.
+        grid_mask = kwargs.get("grid_mask", None)
+        if grid_mask is not None:
+            timestep = timestep[:, grid_mask]
+
+        timestep = timestep * self.timestep_scale_multiplier
+        v_timestep, v_embedded_timestep = self.adaln_single(
+            timestep.flatten(),
+            {"resolution": None, "aspect_ratio": None},
+            batch_size=batch_size,
+            hidden_dtype=hidden_dtype,
+        )
+
+        # Second dimension is 1 or number of tokens (if timestep_per_token)
+        v_timestep = v_timestep.view(batch_size, -1, v_timestep.shape[-1])
+        v_embedded_timestep = v_embedded_timestep.view(
+            batch_size, -1, v_embedded_timestep.shape[-1]
+        )
+
+        # Prepare audio timestep
+        a_timestep = kwargs.get("a_timestep")
+        if a_timestep is not None:
+            a_timestep = a_timestep * self.timestep_scale_multiplier
+            av_ca_factor = self.av_ca_timestep_scale_multiplier / self.timestep_scale_multiplier
+
+            av_ca_audio_scale_shift_timestep, _ = self.av_ca_audio_scale_shift_adaln_single(
+                a_timestep.flatten(),
+                {"resolution": None, "aspect_ratio": None},
+                batch_size=batch_size,
+                hidden_dtype=hidden_dtype,
+            )
+            av_ca_video_scale_shift_timestep, _ = self.av_ca_video_scale_shift_adaln_single(
+                timestep.flatten(),
+                {"resolution": None, "aspect_ratio": None},
+                batch_size=batch_size,
+                hidden_dtype=hidden_dtype,
+            )
+            av_ca_a2v_gate_noise_timestep, _ = self.av_ca_a2v_gate_adaln_single(
+                timestep.flatten() * av_ca_factor,
+                {"resolution": None, "aspect_ratio": None},
+                batch_size=batch_size,
+                hidden_dtype=hidden_dtype,
+            )
+            av_ca_v2a_gate_noise_timestep, _ = self.av_ca_v2a_gate_adaln_single(
+                a_timestep.flatten() * av_ca_factor,
+                {"resolution": None, "aspect_ratio": None},
+                batch_size=batch_size,
+                hidden_dtype=hidden_dtype,
+            )
+
+            a_timestep, a_embedded_timestep = self.audio_adaln_single(
+                a_timestep.flatten(),
+                {"resolution": None, "aspect_ratio": None},
+                batch_size=batch_size,
+                hidden_dtype=hidden_dtype,
+            )
+            a_timestep = a_timestep.view(batch_size, -1, a_timestep.shape[-1])
+            a_embedded_timestep = a_embedded_timestep.view(
+                batch_size, -1, a_embedded_timestep.shape[-1]
+            )
+            cross_av_timestep_ss = [
+                av_ca_audio_scale_shift_timestep,
+                av_ca_video_scale_shift_timestep,
+                av_ca_a2v_gate_noise_timestep,
+                av_ca_v2a_gate_noise_timestep,
+            ]
+            cross_av_timestep_ss = list(
+                [t.view(batch_size, -1, t.shape[-1]) for t in cross_av_timestep_ss]
+            )
+        else:
+            a_timestep = timestep
+            a_embedded_timestep = kwargs.get("embedded_timestep")
+            cross_av_timestep_ss = []
+
+        return [v_timestep, a_timestep, cross_av_timestep_ss], [
+            v_embedded_timestep,
+            a_embedded_timestep,
+        ]
+
+    def _prepare_context(self, context, batch_size, x, attention_mask=None):
+        vx = x[0]
+        ax = x[1]
+        v_context, a_context = torch.split(
+            context, int(context.shape[-1] / 2), len(context.shape) - 1
+        )
+
+        v_context, attention_mask = super()._prepare_context(
+            v_context, batch_size, vx, attention_mask
+        )
+        if self.audio_caption_projection is not None:
+            a_context = self.audio_caption_projection(a_context)
+            a_context = a_context.view(batch_size, -1, ax.shape[-1])
+
+        return [v_context, a_context], attention_mask
+
+    def _prepare_positional_embeddings(self, pixel_coords, frame_rate, x_dtype):
+        v_pixel_coords = pixel_coords[0]
+        v_pe = super()._prepare_positional_embeddings(v_pixel_coords, frame_rate, x_dtype)
+
+        a_latent_coords = pixel_coords[1]
+        a_pe = self._precompute_freqs_cis(
+            a_latent_coords,
+            dim=self.audio_inner_dim,
+            out_dtype=x_dtype,
+            max_pos=self.audio_positional_embedding_max_pos,
+            use_middle_indices_grid=self.use_middle_indices_grid,
+            num_attention_heads=self.audio_num_attention_heads,
+        )
+
+        # calculate positional embeddings for the middle of the token duration, to use in av cross attention layers.
+        max_pos = max(
+            self.positional_embedding_max_pos[0], self.audio_positional_embedding_max_pos[0]
+        )
+        v_pixel_coords = v_pixel_coords.to(torch.float32)
+        v_pixel_coords[:, 0] = v_pixel_coords[:, 0] * (1.0 / frame_rate)
+        av_cross_video_freq_cis = self._precompute_freqs_cis(
+            v_pixel_coords[:, 0:1, :],
+            dim=self.audio_cross_attention_dim,
+            out_dtype=x_dtype,
+            max_pos=[max_pos],
+            use_middle_indices_grid=True,
+            num_attention_heads=self.audio_num_attention_heads,
+        )
+        av_cross_audio_freq_cis = self._precompute_freqs_cis(
+            a_latent_coords[:, 0:1, :],
+            dim=self.audio_cross_attention_dim,
+            out_dtype=x_dtype,
+            max_pos=[max_pos],
+            use_middle_indices_grid=True,
+            num_attention_heads=self.audio_num_attention_heads,
+        )
+
+        return [(v_pe, av_cross_video_freq_cis), (a_pe, av_cross_audio_freq_cis)]
+
+    def _process_transformer_blocks(
+        self, x, context, attention_mask, timestep, pe, transformer_options={}, **kwargs
+    ):
+        vx = x[0]
+        ax = x[1]
+        v_context = context[0]
+        a_context = context[1]
+        v_timestep = timestep[0]
+        a_timestep = timestep[1]
+        v_pe, av_cross_video_freq_cis = pe[0]
+        a_pe, av_cross_audio_freq_cis = pe[1]
+
+        (
+            av_ca_audio_scale_shift_timestep,
+            av_ca_video_scale_shift_timestep,
+            av_ca_a2v_gate_noise_timestep,
+            av_ca_v2a_gate_noise_timestep,
+        ) = timestep[2]
+
+        """Process transformer blocks for LTXAV."""
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+
+        # Process transformer blocks
+        for i, block in enumerate(self.transformer_blocks):
+            if ("double_block", i) in blocks_replace:
+
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(
+                        args["img"],
+                        v_context=args["v_context"],
+                        a_context=args["a_context"],
+                        attention_mask=args["attention_mask"],
+                        v_timestep=args["v_timestep"],
+                        a_timestep=args["a_timestep"],
+                        v_pe=args["v_pe"],
+                        a_pe=args["a_pe"],
+                        v_cross_pe=args["v_cross_pe"],
+                        a_cross_pe=args["a_cross_pe"],
+                        v_cross_scale_shift_timestep=args["v_cross_scale_shift_timestep"],
+                        a_cross_scale_shift_timestep=args["a_cross_scale_shift_timestep"],
+                        v_cross_gate_timestep=args["v_cross_gate_timestep"],
+                        a_cross_gate_timestep=args["a_cross_gate_timestep"],
+                        transformer_options=args["transformer_options"],
+                    )
+                    return out
+
+                out = blocks_replace[("double_block", i)](
+                    {
+                        "img": (vx, ax),
+                        "v_context": v_context,
+                        "a_context": a_context,
+                        "attention_mask": attention_mask,
+                        "v_timestep": v_timestep,
+                        "a_timestep": a_timestep,
+                        "v_pe": v_pe,
+                        "a_pe": a_pe,
+                        "v_cross_pe": av_cross_video_freq_cis,
+                        "a_cross_pe": av_cross_audio_freq_cis,
+                        "v_cross_scale_shift_timestep": av_ca_video_scale_shift_timestep,
+                        "a_cross_scale_shift_timestep": av_ca_audio_scale_shift_timestep,
+                        "v_cross_gate_timestep": av_ca_a2v_gate_noise_timestep,
+                        "a_cross_gate_timestep": av_ca_v2a_gate_noise_timestep,
+                        "transformer_options": transformer_options,
+                    },
+                    {"original_block": block_wrap},
+                )
+                vx, ax = out["img"]
+            else:
+                vx, ax = block(
+                    (vx, ax),
+                    v_context=v_context,
+                    a_context=a_context,
+                    attention_mask=attention_mask,
+                    v_timestep=v_timestep,
+                    a_timestep=a_timestep,
+                    v_pe=v_pe,
+                    a_pe=a_pe,
+                    v_cross_pe=av_cross_video_freq_cis,
+                    a_cross_pe=av_cross_audio_freq_cis,
+                    v_cross_scale_shift_timestep=av_ca_video_scale_shift_timestep,
+                    a_cross_scale_shift_timestep=av_ca_audio_scale_shift_timestep,
+                    v_cross_gate_timestep=av_ca_a2v_gate_noise_timestep,
+                    a_cross_gate_timestep=av_ca_v2a_gate_noise_timestep,
+                    transformer_options=transformer_options,
+                )
+
+        return [vx, ax]
+
+    def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):
+        vx = x[0]
+        ax = x[1]
+        v_embedded_timestep = embedded_timestep[0]
+        a_embedded_timestep = embedded_timestep[1]
+        vx = super()._process_output(vx, v_embedded_timestep, keyframe_idxs, **kwargs)
+
+        # Process audio output
+        a_scale_shift_values = (
+            self.audio_scale_shift_table[None, None].to(device=a_embedded_timestep.device, dtype=a_embedded_timestep.dtype)
+            + a_embedded_timestep[:, :, None]
+        )
+        a_shift, a_scale = a_scale_shift_values[:, :, 0], a_scale_shift_values[:, :, 1]
+
+        ax = self.audio_norm_out(ax)
+        ax = ax * (1 + a_scale) + a_shift
+        ax = self.audio_proj_out(ax)
+
+        # Unpatchify audio
+        ax = self.a_patchifier.unpatchify(
+            ax, channels=self.num_audio_channels, freq=self.audio_frequency_bins
+        )
+
+        # Recombine audio and video
+        original_shape = kwargs.get("av_orig_shape")
+        return self.recombine_audio_and_video_latents(vx, ax, original_shape)
+
+    def forward(
+        self,
+        x,
+        timestep,
+        context,
+        attention_mask=None,
+        frame_rate=25,
+        transformer_options={},
+        keyframe_idxs=None,
+        **kwargs,
+    ):
+        """
+        Forward pass for LTXAV model.
+
+        Args:
+            x: Combined audio-video input tensor
+            timestep: Tuple of (video_timestep, audio_timestep) or single timestep
+            context: Context tensor (e.g., text embeddings)
+            attention_mask: Attention mask tensor
+            frame_rate: Frame rate for temporal processing
+            transformer_options: Additional options for transformer blocks
+            keyframe_idxs: Keyframe indices for temporal processing
+            **kwargs: Additional keyword arguments including audio_length
+
+        Returns:
+            Combined audio-video output tensor
+        """
+        # Handle timestep format
+        if isinstance(timestep, (tuple, list)) and len(timestep) == 2:
+            v_timestep, a_timestep = timestep
+            kwargs["a_timestep"] = a_timestep
+            timestep = v_timestep
+        else:
+            kwargs["a_timestep"] = timestep
+
+        # Call parent forward method
+        return super().forward(
+            x,
+            timestep,
+            context,
+            attention_mask,
+            frame_rate,
+            transformer_options,
+            keyframe_idxs,
+            **kwargs,
+        )

comfy/ldm/lightricks/embeddings_connector.py

@@ -0,0 +1,305 @@
+import math
+from typing import Optional
+
+import comfy.ldm.common_dit
+import torch
+from comfy.ldm.lightricks.model import (
+    CrossAttention,
+    FeedForward,
+    generate_freq_grid_np,
+    interleaved_freqs_cis,
+    split_freqs_cis,
+)
+from torch import nn
+
+
+class BasicTransformerBlock1D(nn.Module):
+    r"""
+    A basic Transformer block.
+
+    Parameters:
+
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        attention_bias (:
+            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+        upcast_attention (`bool`, *optional*):
+            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
+        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        standardization_norm (`str`, *optional*, defaults to `"layer_norm"`): The type of pre-normalization to use. Can be `"layer_norm"` or `"rms_norm"`.
+        norm_eps (`float`, *optional*, defaults to 1e-5): Epsilon value for normalization layers.
+        qk_norm (`str`, *optional*, defaults to None):
+            Set to 'layer_norm' or `rms_norm` to perform query and key normalization.
+        final_dropout (`bool` *optional*, defaults to False):
+            Whether to apply a final dropout after the last feed-forward layer.
+        ff_inner_dim (`int`, *optional*): Dimension of the inner feed-forward layer. If not provided, defaults to `dim * 4`.
+        ff_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in the feed-forward layer.
+        attention_out_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in the attention output layer.
+        use_rope (`bool`, *optional*, defaults to `False`): Whether to use Rotary Position Embeddings (RoPE).
+        ffn_dim_mult (`int`, *optional*, defaults to 4): Multiplier for the inner dimension of the feed-forward layer.
+    """
+
+    def __init__(
+        self,
+        dim,
+        n_heads,
+        d_head,
+        context_dim=None,
+        attn_precision=None,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+
+        # Define 3 blocks. Each block has its own normalization layer.
+        # 1. Self-Attn
+        self.attn1 = CrossAttention(
+            query_dim=dim,
+            heads=n_heads,
+            dim_head=d_head,
+            context_dim=None,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        # 3. Feed-forward
+        self.ff = FeedForward(
+            dim,
+            dim_out=dim,
+            glu=True,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+    def forward(self, hidden_states, attention_mask=None, pe=None) -> torch.FloatTensor:
+
+        # Notice that normalization is always applied before the real computation in the following blocks.
+
+        # 1. Normalization Before Self-Attention
+        norm_hidden_states = comfy.ldm.common_dit.rms_norm(hidden_states)
+
+        norm_hidden_states = norm_hidden_states.squeeze(1)
+
+        # 2. Self-Attention
+        attn_output = self.attn1(norm_hidden_states, mask=attention_mask, pe=pe)
+
+        hidden_states = attn_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        # 3. Normalization before Feed-Forward
+        norm_hidden_states = comfy.ldm.common_dit.rms_norm(hidden_states)
+
+        # 4. Feed-forward
+        ff_output = self.ff(norm_hidden_states)
+
+        hidden_states = ff_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        return hidden_states
+
+
+class Embeddings1DConnector(nn.Module):
+    _supports_gradient_checkpointing = True
+
+    def __init__(
+        self,
+        in_channels=128,
+        cross_attention_dim=2048,
+        attention_head_dim=128,
+        num_attention_heads=30,
+        num_layers=2,
+        positional_embedding_theta=10000.0,
+        positional_embedding_max_pos=[4096],
+        causal_temporal_positioning=False,
+        num_learnable_registers: Optional[int] = 128,
+        dtype=None,
+        device=None,
+        operations=None,
+        split_rope=False,
+        double_precision_rope=False,
+        **kwargs,
+    ):
+        super().__init__()
+        self.dtype = dtype
+        self.out_channels = in_channels
+        self.num_attention_heads = num_attention_heads
+        self.inner_dim = num_attention_heads * attention_head_dim
+        self.causal_temporal_positioning = causal_temporal_positioning
+        self.positional_embedding_theta = positional_embedding_theta
+        self.positional_embedding_max_pos = positional_embedding_max_pos
+        self.split_rope = split_rope
+        self.double_precision_rope = double_precision_rope
+        self.transformer_1d_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock1D(
+                    self.inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    context_dim=cross_attention_dim,
+                    dtype=dtype,
+                    device=device,
+                    operations=operations,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        inner_dim = num_attention_heads * attention_head_dim
+        self.num_learnable_registers = num_learnable_registers
+        if self.num_learnable_registers:
+            self.learnable_registers = nn.Parameter(
+                torch.rand(
+                    self.num_learnable_registers, inner_dim, dtype=dtype, device=device
+                )
+                * 2.0
+                - 1.0
+            )
+
+    def get_fractional_positions(self, indices_grid):
+        fractional_positions = torch.stack(
+            [
+                indices_grid[:, i] / self.positional_embedding_max_pos[i]
+                for i in range(1)
+            ],
+            dim=-1,
+        )
+        return fractional_positions
+
+    def precompute_freqs(self, indices_grid, spacing):
+        source_dtype = indices_grid.dtype
+        dtype = (
+            torch.float32
+            if source_dtype in (torch.bfloat16, torch.float16)
+            else source_dtype
+        )
+
+        fractional_positions = self.get_fractional_positions(indices_grid)
+        indices = (
+            generate_freq_grid_np(
+                self.positional_embedding_theta,
+                indices_grid.shape[1],
+                self.inner_dim,
+            )
+            if self.double_precision_rope
+            else self.generate_freq_grid(spacing, dtype, fractional_positions.device)
+        ).to(device=fractional_positions.device)
+
+        if spacing == "exp_2":
+            freqs = (
+                (indices * fractional_positions.unsqueeze(-1))
+                .transpose(-1, -2)
+                .flatten(2)
+            )
+        else:
+            freqs = (
+                (indices * (fractional_positions.unsqueeze(-1) * 2 - 1))
+                .transpose(-1, -2)
+                .flatten(2)
+            )
+        return freqs
+
+    def generate_freq_grid(self, spacing, dtype, device):
+        dim = self.inner_dim
+        theta = self.positional_embedding_theta
+        n_pos_dims = 1
+        n_elem = 2 * n_pos_dims  # 2 for cos and sin e.g. x 3 = 6
+        start = 1
+        end = theta
+
+        if spacing == "exp":
+            indices = theta ** (torch.arange(0, dim, n_elem, device="cpu", dtype=torch.float32) / (dim - n_elem))
+            indices = indices.to(dtype=dtype, device=device)
+        elif spacing == "exp_2":
+            indices = 1.0 / theta ** (torch.arange(0, dim, n_elem, device=device) / dim)
+            indices = indices.to(dtype=dtype)
+        elif spacing == "linear":
+            indices = torch.linspace(
+                start, end, dim // n_elem, device=device, dtype=dtype
+            )
+        elif spacing == "sqrt":
+            indices = torch.linspace(
+                start**2, end**2, dim // n_elem, device=device, dtype=dtype
+            ).sqrt()
+
+        indices = indices * math.pi / 2
+
+        return indices
+
+    def precompute_freqs_cis(self, indices_grid, spacing="exp"):
+        dim = self.inner_dim
+        n_elem = 2  # 2 because of cos and sin
+        freqs = self.precompute_freqs(indices_grid, spacing)
+        if self.split_rope:
+            expected_freqs = dim // 2
+            current_freqs = freqs.shape[-1]
+            pad_size = expected_freqs - current_freqs
+            cos_freq, sin_freq = split_freqs_cis(
+                freqs, pad_size, self.num_attention_heads
+            )
+        else:
+            cos_freq, sin_freq = interleaved_freqs_cis(freqs, dim % n_elem)
+        return cos_freq.to(self.dtype), sin_freq.to(self.dtype), self.split_rope
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        """
+        The [`Transformer2DModel`] forward method.
+
+        Args:
+            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
+                Input `hidden_states`.
+            indices_grid (`torch.LongTensor` of shape `(batch size, 3, num latent pixels)`):
+            attention_mask ( `torch.Tensor`, *optional*):
+                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
+                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
+                negative values to the attention scores corresponding to "discard" tokens.
+        Returns:
+            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+            `tuple` where the first element is the sample tensor.
+        """
+        # 1. Input
+
+        if self.num_learnable_registers:
+            num_registers_duplications = math.ceil(
+                max(1024, hidden_states.shape[1]) / self.num_learnable_registers
+            )
+            learnable_registers = torch.tile(
+                self.learnable_registers, (num_registers_duplications, 1)
+            )
+
+            hidden_states = torch.cat((hidden_states, learnable_registers[hidden_states.shape[1]:].unsqueeze(0).repeat(hidden_states.shape[0], 1, 1)), dim=1)
+
+            if attention_mask is not None:
+                attention_mask = torch.zeros([1, 1, 1, hidden_states.shape[1]], dtype=attention_mask.dtype, device=attention_mask.device)
+
+        indices_grid = torch.arange(
+            hidden_states.shape[1], dtype=torch.float32, device=hidden_states.device
+        )
+        indices_grid = indices_grid[None, None, :]
+        freqs_cis = self.precompute_freqs_cis(indices_grid)
+
+        # 2. Blocks
+        for block_idx, block in enumerate(self.transformer_1d_blocks):
+            hidden_states = block(
+                hidden_states, attention_mask=attention_mask, pe=freqs_cis
+            )
+
+        # 3. Output
+        # if self.output_scale is not None:
+        #     hidden_states = hidden_states / self.output_scale
+
+        hidden_states = comfy.ldm.common_dit.rms_norm(hidden_states)
+
+        return hidden_states, attention_mask

comfy/ldm/lightricks/latent_upsampler.py

@@ -0,0 +1,292 @@
+from typing import Optional, Tuple
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+
+def _rational_for_scale(scale: float) -> Tuple[int, int]:
+    mapping = {0.75: (3, 4), 1.5: (3, 2), 2.0: (2, 1), 4.0: (4, 1)}
+    if float(scale) not in mapping:
+        raise ValueError(
+            f"Unsupported spatial_scale {scale}. Choose from {list(mapping.keys())}"
+        )
+    return mapping[float(scale)]
+
+
+class PixelShuffleND(nn.Module):
+    def __init__(self, dims, upscale_factors=(2, 2, 2)):
+        super().__init__()
+        assert dims in [1, 2, 3], "dims must be 1, 2, or 3"
+        self.dims = dims
+        self.upscale_factors = upscale_factors
+
+    def forward(self, x):
+        if self.dims == 3:
+            return rearrange(
+                x,
+                "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)",
+                p1=self.upscale_factors[0],
+                p2=self.upscale_factors[1],
+                p3=self.upscale_factors[2],
+            )
+        elif self.dims == 2:
+            return rearrange(
+                x,
+                "b (c p1 p2) h w -> b c (h p1) (w p2)",
+                p1=self.upscale_factors[0],
+                p2=self.upscale_factors[1],
+            )
+        elif self.dims == 1:
+            return rearrange(
+                x,
+                "b (c p1) f h w -> b c (f p1) h w",
+                p1=self.upscale_factors[0],
+            )
+
+
+class BlurDownsample(nn.Module):
+    """
+    Anti-aliased spatial downsampling by integer stride using a fixed separable binomial kernel.
+    Applies only on H,W. Works for dims=2 or dims=3 (per-frame).
+    """
+
+    def __init__(self, dims: int, stride: int):
+        super().__init__()
+        assert dims in (2, 3)
+        assert stride >= 1 and isinstance(stride, int)
+        self.dims = dims
+        self.stride = stride
+
+        # 5x5 separable binomial kernel [1,4,6,4,1] (outer product), normalized
+        k = torch.tensor([1.0, 4.0, 6.0, 4.0, 1.0])
+        k2d = k[:, None] @ k[None, :]
+        k2d = (k2d / k2d.sum()).float()  # shape (5,5)
+        self.register_buffer("kernel", k2d[None, None, :, :])  # (1,1,5,5)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.stride == 1:
+            return x
+
+        def _apply_2d(x2d: torch.Tensor) -> torch.Tensor:
+            # x2d: (B, C, H, W)
+            B, C, H, W = x2d.shape
+            weight = self.kernel.expand(C, 1, 5, 5)  # depthwise
+            x2d = F.conv2d(
+                x2d, weight=weight, bias=None, stride=self.stride, padding=2, groups=C
+            )
+            return x2d
+
+        if self.dims == 2:
+            return _apply_2d(x)
+        else:
+            # dims == 3: apply per-frame on H,W
+            b, c, f, h, w = x.shape
+            x = rearrange(x, "b c f h w -> (b f) c h w")
+            x = _apply_2d(x)
+            h2, w2 = x.shape[-2:]
+            x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f, h=h2, w=w2)
+            return x
+
+
+class SpatialRationalResampler(nn.Module):
+    """
+    Fully-learned rational spatial scaling: up by 'num' via PixelShuffle, then anti-aliased
+    downsample by 'den' using fixed blur + stride. Operates on H,W only.
+
+    For dims==3, work per-frame for spatial scaling (temporal axis untouched).
+    """
+
+    def __init__(self, mid_channels: int, scale: float):
+        super().__init__()
+        self.scale = float(scale)
+        self.num, self.den = _rational_for_scale(self.scale)
+        self.conv = nn.Conv2d(
+            mid_channels, (self.num**2) * mid_channels, kernel_size=3, padding=1
+        )
+        self.pixel_shuffle = PixelShuffleND(2, upscale_factors=(self.num, self.num))
+        self.blur_down = BlurDownsample(dims=2, stride=self.den)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        b, c, f, h, w = x.shape
+        x = rearrange(x, "b c f h w -> (b f) c h w")
+        x = self.conv(x)
+        x = self.pixel_shuffle(x)
+        x = self.blur_down(x)
+        x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f)
+        return x
+
+
+class ResBlock(nn.Module):
+    def __init__(
+        self, channels: int, mid_channels: Optional[int] = None, dims: int = 3
+    ):
+        super().__init__()
+        if mid_channels is None:
+            mid_channels = channels
+
+        Conv = nn.Conv2d if dims == 2 else nn.Conv3d
+
+        self.conv1 = Conv(channels, mid_channels, kernel_size=3, padding=1)
+        self.norm1 = nn.GroupNorm(32, mid_channels)
+        self.conv2 = Conv(mid_channels, channels, kernel_size=3, padding=1)
+        self.norm2 = nn.GroupNorm(32, channels)
+        self.activation = nn.SiLU()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.activation(x)
+        x = self.conv2(x)
+        x = self.norm2(x)
+        x = self.activation(x + residual)
+        return x
+
+
+class LatentUpsampler(nn.Module):
+    """
+    Model to spatially upsample VAE latents.
+
+    Args:
+        in_channels (`int`): Number of channels in the input latent
+        mid_channels (`int`): Number of channels in the middle layers
+        num_blocks_per_stage (`int`): Number of ResBlocks to use in each stage (pre/post upsampling)
+        dims (`int`): Number of dimensions for convolutions (2 or 3)
+        spatial_upsample (`bool`): Whether to spatially upsample the latent
+        temporal_upsample (`bool`): Whether to temporally upsample the latent
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 128,
+        mid_channels: int = 512,
+        num_blocks_per_stage: int = 4,
+        dims: int = 3,
+        spatial_upsample: bool = True,
+        temporal_upsample: bool = False,
+        spatial_scale: float = 2.0,
+        rational_resampler: bool = False,
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.mid_channels = mid_channels
+        self.num_blocks_per_stage = num_blocks_per_stage
+        self.dims = dims
+        self.spatial_upsample = spatial_upsample
+        self.temporal_upsample = temporal_upsample
+        self.spatial_scale = float(spatial_scale)
+        self.rational_resampler = rational_resampler
+
+        Conv = nn.Conv2d if dims == 2 else nn.Conv3d
+
+        self.initial_conv = Conv(in_channels, mid_channels, kernel_size=3, padding=1)
+        self.initial_norm = nn.GroupNorm(32, mid_channels)
+        self.initial_activation = nn.SiLU()
+
+        self.res_blocks = nn.ModuleList(
+            [ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)]
+        )
+
+        if spatial_upsample and temporal_upsample:
+            self.upsampler = nn.Sequential(
+                nn.Conv3d(mid_channels, 8 * mid_channels, kernel_size=3, padding=1),
+                PixelShuffleND(3),
+            )
+        elif spatial_upsample:
+            if rational_resampler:
+                self.upsampler = SpatialRationalResampler(
+                    mid_channels=mid_channels, scale=self.spatial_scale
+                )
+            else:
+                self.upsampler = nn.Sequential(
+                    nn.Conv2d(mid_channels, 4 * mid_channels, kernel_size=3, padding=1),
+                    PixelShuffleND(2),
+                )
+        elif temporal_upsample:
+            self.upsampler = nn.Sequential(
+                nn.Conv3d(mid_channels, 2 * mid_channels, kernel_size=3, padding=1),
+                PixelShuffleND(1),
+            )
+        else:
+            raise ValueError(
+                "Either spatial_upsample or temporal_upsample must be True"
+            )
+
+        self.post_upsample_res_blocks = nn.ModuleList(
+            [ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)]
+        )
+
+        self.final_conv = Conv(mid_channels, in_channels, kernel_size=3, padding=1)
+
+    def forward(self, latent: torch.Tensor) -> torch.Tensor:
+        b, c, f, h, w = latent.shape
+
+        if self.dims == 2:
+            x = rearrange(latent, "b c f h w -> (b f) c h w")
+            x = self.initial_conv(x)
+            x = self.initial_norm(x)
+            x = self.initial_activation(x)
+
+            for block in self.res_blocks:
+                x = block(x)
+
+            x = self.upsampler(x)
+
+            for block in self.post_upsample_res_blocks:
+                x = block(x)
+
+            x = self.final_conv(x)
+            x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f)
+        else:
+            x = self.initial_conv(latent)
+            x = self.initial_norm(x)
+            x = self.initial_activation(x)
+
+            for block in self.res_blocks:
+                x = block(x)
+
+            if self.temporal_upsample:
+                x = self.upsampler(x)
+                x = x[:, :, 1:, :, :]
+            else:
+                if isinstance(self.upsampler, SpatialRationalResampler):
+                    x = self.upsampler(x)
+                else:
+                    x = rearrange(x, "b c f h w -> (b f) c h w")
+                    x = self.upsampler(x)
+                    x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f)
+
+            for block in self.post_upsample_res_blocks:
+                x = block(x)
+
+            x = self.final_conv(x)
+
+        return x
+
+    @classmethod
+    def from_config(cls, config):
+        return cls(
+            in_channels=config.get("in_channels", 4),
+            mid_channels=config.get("mid_channels", 128),
+            num_blocks_per_stage=config.get("num_blocks_per_stage", 4),
+            dims=config.get("dims", 2),
+            spatial_upsample=config.get("spatial_upsample", True),
+            temporal_upsample=config.get("temporal_upsample", False),
+            spatial_scale=config.get("spatial_scale", 2.0),
+            rational_resampler=config.get("rational_resampler", False),
+        )
+
+    def config(self):
+        return {
+            "_class_name": "LatentUpsampler",
+            "in_channels": self.in_channels,
+            "mid_channels": self.mid_channels,
+            "num_blocks_per_stage": self.num_blocks_per_stage,
+            "dims": self.dims,
+            "spatial_upsample": self.spatial_upsample,
+            "temporal_upsample": self.temporal_upsample,
+            "spatial_scale": self.spatial_scale,
+            "rational_resampler": self.rational_resampler,
+        }

comfy/ldm/lightricks/model.py

@@ -1,13 +1,47 @@
+from abc import ABC, abstractmethod
+from enum import Enum
+import functools
+import math
+from typing import Dict, Optional, Tuple
+
+from einops import rearrange
+import numpy as np
 import torch
 from torch import nn
 import comfy.patcher_extension
 import comfy.ldm.modules.attention
 import comfy.ldm.common_dit
-import math
-from typing import Dict, Optional, Tuple
 
 from .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords
-from comfy.ldm.flux.math import apply_rope1
+
+def _log_base(x, base):
+    return np.log(x) / np.log(base)
+
+class LTXRopeType(str, Enum):
+    INTERLEAVED = "interleaved"
+    SPLIT = "split"
+
+    KEY = "rope_type"
+
+    @classmethod
+    def from_dict(cls, kwargs, default=None):
+        if default is None:
+            default = cls.INTERLEAVED
+        return cls(kwargs.get(cls.KEY, default))
+
+
+class LTXFrequenciesPrecision(str, Enum):
+    FLOAT32 = "float32"
+    FLOAT64 = "float64"
+
+    KEY = "frequencies_precision"
+
+    @classmethod
+    def from_dict(cls, kwargs, default=None):
+        if default is None:
+            default = cls.FLOAT32
+        return cls(kwargs.get(cls.KEY, default))
+
 
 def get_timestep_embedding(
     timesteps: torch.Tensor,
@@ -39,9 +73,7 @@ def get_timestep_embedding(
     assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
 
     half_dim = embedding_dim // 2
-    exponent = -math.log(max_period) * torch.arange(
+    exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device)
-        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
-    )
     exponent = exponent / (half_dim - downscale_freq_shift)
 
     emb = torch.exp(exponent)
@@ -73,7 +105,9 @@ class TimestepEmbedding(nn.Module):
         post_act_fn: Optional[str] = None,
         cond_proj_dim=None,
         sample_proj_bias=True,
-        dtype=None, device=None, operations=None,
+        dtype=None,
+        device=None,
+        operations=None,
     ):
         super().__init__()
 
@@ -90,7 +124,9 @@ class TimestepEmbedding(nn.Module):
             time_embed_dim_out = out_dim
         else:
             time_embed_dim_out = time_embed_dim
-        self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim_out, sample_proj_bias, dtype=dtype, device=device)
+        self.linear_2 = operations.Linear(
+            time_embed_dim, time_embed_dim_out, sample_proj_bias, dtype=dtype, device=device
+        )
 
         if post_act_fn is None:
             self.post_act = None
@@ -139,12 +175,22 @@ class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
     https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29
     """
 
-    def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False, dtype=None, device=None, operations=None):
+    def __init__(
+        self,
+        embedding_dim,
+        size_emb_dim,
+        use_additional_conditions: bool = False,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
         super().__init__()
 
         self.outdim = size_emb_dim
         self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
-        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim, dtype=dtype, device=device, operations=operations)
+        self.timestep_embedder = TimestepEmbedding(
+            in_channels=256, time_embed_dim=embedding_dim, dtype=dtype, device=device, operations=operations
+        )
 
     def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype):
         timesteps_proj = self.time_proj(timestep)
@@ -163,15 +209,22 @@ class AdaLayerNormSingle(nn.Module):
         use_additional_conditions (`bool`): To use additional conditions for normalization or not.
     """
 
-    def __init__(self, embedding_dim: int, use_additional_conditions: bool = False, dtype=None, device=None, operations=None):
+    def __init__(
+        self, embedding_dim: int, embedding_coefficient: int = 6, use_additional_conditions: bool = False, dtype=None, device=None, operations=None
+    ):
         super().__init__()
 
         self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(
-            embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions, dtype=dtype, device=device, operations=operations
+            embedding_dim,
+            size_emb_dim=embedding_dim // 3,
+            use_additional_conditions=use_additional_conditions,
+            dtype=dtype,
+            device=device,
+            operations=operations,
         )
 
         self.silu = nn.SiLU()
-        self.linear = operations.Linear(embedding_dim, 6 * embedding_dim, bias=True, dtype=dtype, device=device)
+        self.linear = operations.Linear(embedding_dim, embedding_coefficient * embedding_dim, bias=True, dtype=dtype, device=device)
 
     def forward(
         self,
@@ -185,6 +238,7 @@ class AdaLayerNormSingle(nn.Module):
         embedded_timestep = self.emb(timestep, **added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_dtype)
         return self.linear(self.silu(embedded_timestep)), embedded_timestep
 
+
 class PixArtAlphaTextProjection(nn.Module):
     """
     Projects caption embeddings. Also handles dropout for classifier-free guidance.
@@ -192,18 +246,24 @@ class PixArtAlphaTextProjection(nn.Module):
     Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
     """
 
-    def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh", dtype=None, device=None, operations=None):
+    def __init__(
+        self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh", dtype=None, device=None, operations=None
+    ):
         super().__init__()
         if out_features is None:
             out_features = hidden_size
-        self.linear_1 = operations.Linear(in_features=in_features, out_features=hidden_size, bias=True, dtype=dtype, device=device)
+        self.linear_1 = operations.Linear(
+            in_features=in_features, out_features=hidden_size, bias=True, dtype=dtype, device=device
+        )
         if act_fn == "gelu_tanh":
             self.act_1 = nn.GELU(approximate="tanh")
         elif act_fn == "silu":
             self.act_1 = nn.SiLU()
         else:
             raise ValueError(f"Unknown activation function: {act_fn}")
-        self.linear_2 = operations.Linear(in_features=hidden_size, out_features=out_features, bias=True, dtype=dtype, device=device)
+        self.linear_2 = operations.Linear(
+            in_features=hidden_size, out_features=out_features, bias=True, dtype=dtype, device=device
+        )
 
     def forward(self, caption):
         hidden_states = self.linear_1(caption)
@@ -222,23 +282,68 @@ class GELU_approx(nn.Module):
 
 
 class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=None):
+    def __init__(self, dim, dim_out, mult=4, glu=False, dropout=0.0, dtype=None, device=None, operations=None):
         super().__init__()
         inner_dim = int(dim * mult)
         project_in = GELU_approx(dim, inner_dim, dtype=dtype, device=device, operations=operations)
 
         self.net = nn.Sequential(
-            project_in,
+            project_in, nn.Dropout(dropout), operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
-            nn.Dropout(dropout),
-            operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
         )
 
     def forward(self, x):
         return self.net(x)
 
+def apply_rotary_emb(input_tensor, freqs_cis):
+    cos_freqs, sin_freqs = freqs_cis[0], freqs_cis[1]
+    split_pe = freqs_cis[2] if len(freqs_cis) > 2 else False
+    return (
+        apply_split_rotary_emb(input_tensor, cos_freqs, sin_freqs)
+        if split_pe else
+        apply_interleaved_rotary_emb(input_tensor, cos_freqs, sin_freqs)
+    )
+
+def apply_interleaved_rotary_emb(input_tensor, cos_freqs, sin_freqs):  # TODO: remove duplicate funcs and pick the best/fastest one
+    t_dup = rearrange(input_tensor, "... (d r) -> ... d r", r=2)
+    t1, t2 = t_dup.unbind(dim=-1)
+    t_dup = torch.stack((-t2, t1), dim=-1)
+    input_tensor_rot = rearrange(t_dup, "... d r -> ... (d r)")
+
+    out = input_tensor * cos_freqs + input_tensor_rot * sin_freqs
+
+    return out
+
+def apply_split_rotary_emb(input_tensor, cos, sin):
+    needs_reshape = False
+    if input_tensor.ndim != 4 and cos.ndim == 4:
+        B, H, T, _ = cos.shape
+        input_tensor = input_tensor.reshape(B, T, H, -1).swapaxes(1, 2)
+        needs_reshape = True
+    split_input = rearrange(input_tensor, "... (d r) -> ... d r", d=2)
+    first_half_input = split_input[..., :1, :]
+    second_half_input = split_input[..., 1:, :]
+    output = split_input * cos.unsqueeze(-2)
+    first_half_output = output[..., :1, :]
+    second_half_output = output[..., 1:, :]
+    first_half_output.addcmul_(-sin.unsqueeze(-2), second_half_input)
+    second_half_output.addcmul_(sin.unsqueeze(-2), first_half_input)
+    output = rearrange(output, "... d r -> ... (d r)")
+    return output.swapaxes(1, 2).reshape(B, T, -1) if needs_reshape else output
+
 
 class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=None):
+    def __init__(
+        self,
+        query_dim,
+        context_dim=None,
+        heads=8,
+        dim_head=64,
+        dropout=0.0,
+        attn_precision=None,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = query_dim if context_dim is None else context_dim
@@ -254,9 +359,11 @@ class CrossAttention(nn.Module):
         self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
         self.to_v = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
 
-        self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
+        self.to_out = nn.Sequential(
+            operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)
+        )
 
-    def forward(self, x, context=None, mask=None, pe=None, transformer_options={}):
+    def forward(self, x, context=None, mask=None, pe=None, k_pe=None, transformer_options={}):
         q = self.to_q(x)
         context = x if context is None else context
         k = self.to_k(context)
@@ -266,8 +373,8 @@ class CrossAttention(nn.Module):
         k = self.k_norm(k)
 
         if pe is not None:
-            q = apply_rope1(q.unsqueeze(1), pe).squeeze(1)
+            q = apply_rotary_emb(q, pe)
-            k = apply_rope1(k.unsqueeze(1), pe).squeeze(1)
+            k = apply_rotary_emb(k, pe if k_pe is None else k_pe)
 
         if mask is None:
             out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
@@ -277,14 +384,34 @@ class CrossAttention(nn.Module):
 
 
 class BasicTransformerBlock(nn.Module):
-    def __init__(self, dim, n_heads, d_head, context_dim=None, attn_precision=None, dtype=None, device=None, operations=None):
+    def __init__(
+        self, dim, n_heads, d_head, context_dim=None, attn_precision=None, dtype=None, device=None, operations=None
+    ):
         super().__init__()
 
         self.attn_precision = attn_precision
-        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, context_dim=None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)
+        self.attn1 = CrossAttention(
+            query_dim=dim,
+            heads=n_heads,
+            dim_head=d_head,
+            context_dim=None,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
         self.ff = FeedForward(dim, dim_out=dim, glu=True, dtype=dtype, device=device, operations=operations)
 
-        self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)
+        self.attn2 = CrossAttention(
+            query_dim=dim,
+            context_dim=context_dim,
+            heads=n_heads,
+            dim_head=d_head,
+            attn_precision=self.attn_precision,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
 
         self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype))
 
@@ -306,116 +433,446 @@ class BasicTransformerBlock(nn.Module):
         return x
 
 def get_fractional_positions(indices_grid, max_pos):
+    n_pos_dims = indices_grid.shape[1]
+    assert n_pos_dims == len(max_pos), f'Number of position dimensions ({n_pos_dims}) must match max_pos length ({len(max_pos)})'
     fractional_positions = torch.stack(
-        [
+        [indices_grid[:, i] / max_pos[i] for i in range(n_pos_dims)],
-            indices_grid[:, i] / max_pos[i]
+        axis=-1,
-            for i in range(3)
-        ],
-        dim=-1,
     )
     return fractional_positions
 
 
-def precompute_freqs_cis(indices_grid, dim, out_dtype, theta=10000.0, max_pos=[20, 2048, 2048]):
+@functools.lru_cache(maxsize=5)
-    dtype = torch.float32
+def generate_freq_grid_np(positional_embedding_theta, positional_embedding_max_pos_count, inner_dim, _ = None):
-    device = indices_grid.device
+    theta = positional_embedding_theta
+    start = 1
+    end = theta
+
+    n_elem = 2 * positional_embedding_max_pos_count
+    pow_indices = np.power(
+        theta,
+        np.linspace(
+            _log_base(start, theta),
+            _log_base(end, theta),
+            inner_dim // n_elem,
+            dtype=np.float64,
+        ),
+    )
+    return torch.tensor(pow_indices * math.pi / 2, dtype=torch.float32)
+
+def generate_freq_grid_pytorch(positional_embedding_theta, positional_embedding_max_pos_count, inner_dim, device):
+    theta = positional_embedding_theta
+    start = 1
+    end = theta
+    n_elem = 2 * positional_embedding_max_pos_count
+
+    indices = theta ** (
+        torch.linspace(
+            math.log(start, theta),
+            math.log(end, theta),
+            inner_dim // n_elem,
+            device=device,
+            dtype=torch.float32,
+        )
+    )
+    indices = indices.to(dtype=torch.float32)
+
+    indices = indices * math.pi / 2
+
+    return indices
+
+def generate_freqs(indices, indices_grid, max_pos, use_middle_indices_grid):
+    if use_middle_indices_grid:
+        assert(len(indices_grid.shape) == 4 and indices_grid.shape[-1] ==2)
+        indices_grid_start, indices_grid_end = indices_grid[..., 0], indices_grid[..., 1]
+        indices_grid = (indices_grid_start + indices_grid_end) / 2.0
+    elif len(indices_grid.shape) == 4:
+        indices_grid = indices_grid[..., 0]
 
     # Get fractional positions and compute frequency indices
     fractional_positions = get_fractional_positions(indices_grid, max_pos)
-    indices = theta ** torch.linspace(0, 1, dim // 6, device=device, dtype=dtype) * math.pi / 2
+    indices = indices.to(device=fractional_positions.device)
 
-    # Compute frequencies and apply cos/sin
+    freqs = (
-    freqs = (indices * (fractional_positions.unsqueeze(-1) * 2 - 1)).transpose(-1, -2).flatten(2)
+        (indices * (fractional_positions.unsqueeze(-1) * 2 - 1))
-    cos_vals = freqs.cos().repeat_interleave(2, dim=-1)
+        .transpose(-1, -2)
-    sin_vals = freqs.sin().repeat_interleave(2, dim=-1)
+        .flatten(2)
+    )
+    return freqs
 
-    # Pad if dim is not divisible by 6
+def interleaved_freqs_cis(freqs, pad_size):
-    if dim % 6 != 0:
+    cos_freq = freqs.cos().repeat_interleave(2, dim=-1)
-        padding_size = dim % 6
+    sin_freq = freqs.sin().repeat_interleave(2, dim=-1)
-        cos_vals = torch.cat([torch.ones_like(cos_vals[:, :, :padding_size]), cos_vals], dim=-1)
+    if pad_size != 0:
-        sin_vals = torch.cat([torch.zeros_like(sin_vals[:, :, :padding_size]), sin_vals], dim=-1)
+        cos_padding = torch.ones_like(cos_freq[:, :, : pad_size])
+        sin_padding = torch.zeros_like(cos_freq[:, :, : pad_size])
+        cos_freq = torch.cat([cos_padding, cos_freq], dim=-1)
+        sin_freq = torch.cat([sin_padding, sin_freq], dim=-1)
+    return cos_freq, sin_freq
 
-    # Reshape and extract one value per pair (since repeat_interleave duplicates each value)
+def split_freqs_cis(freqs, pad_size, num_attention_heads):
-    cos_vals = cos_vals.reshape(*cos_vals.shape[:2], -1, 2)[..., 0].to(out_dtype)  # [B, N, dim//2]
+    cos_freq = freqs.cos()
-    sin_vals = sin_vals.reshape(*sin_vals.shape[:2], -1, 2)[..., 0].to(out_dtype)  # [B, N, dim//2]
+    sin_freq = freqs.sin()
 
-    # Build rotation matrix [[cos, -sin], [sin, cos]] and add heads dimension
+    if pad_size != 0:
-    freqs_cis = torch.stack([
+        cos_padding = torch.ones_like(cos_freq[:, :, :pad_size])
-        torch.stack([cos_vals, -sin_vals], dim=-1),
+        sin_padding = torch.zeros_like(sin_freq[:, :, :pad_size])
-        torch.stack([sin_vals, cos_vals], dim=-1)
-    ], dim=-2).unsqueeze(1)  # [B, 1, N, dim//2, 2, 2]
 
-    return freqs_cis
+        cos_freq = torch.concatenate([cos_padding, cos_freq], axis=-1)
+        sin_freq = torch.concatenate([sin_padding, sin_freq], axis=-1)
 
+    # Reshape freqs to be compatible with multi-head attention
+    B , T, half_HD = cos_freq.shape
 
-class LTXVModel(torch.nn.Module):
+    cos_freq = cos_freq.reshape(B, T, num_attention_heads, half_HD // num_attention_heads)
-    def __init__(self,
+    sin_freq = sin_freq.reshape(B, T, num_attention_heads, half_HD // num_attention_heads)
-                 in_channels=128,
-                 cross_attention_dim=2048,
-                 attention_head_dim=64,
-                 num_attention_heads=32,
 
-                 caption_channels=4096,
+    cos_freq = torch.swapaxes(cos_freq, 1, 2)  # (B,H,T,D//2)
-                 num_layers=28,
+    sin_freq = torch.swapaxes(sin_freq, 1, 2)  # (B,H,T,D//2)
+    return cos_freq, sin_freq
 
+class LTXBaseModel(torch.nn.Module, ABC):
+    """
+    Abstract base class for LTX models (Lightricks Transformer models).
 
-                 positional_embedding_theta=10000.0,
+    This class defines the common interface and shared functionality for all LTX models,
-                 positional_embedding_max_pos=[20, 2048, 2048],
+    including LTXV (video) and LTXAV (audio-video) variants.
-                 causal_temporal_positioning=False,
+    """
-                 vae_scale_factors=(8, 32, 32),
+
-                 dtype=None, device=None, operations=None, **kwargs):
+    def __init__(
+        self,
+        in_channels: int,
+        cross_attention_dim: int,
+        attention_head_dim: int,
+        num_attention_heads: int,
+        caption_channels: int,
+        num_layers: int,
+        positional_embedding_theta: float = 10000.0,
+        positional_embedding_max_pos: list = [20, 2048, 2048],
+        causal_temporal_positioning: bool = False,
+        vae_scale_factors: tuple = (8, 32, 32),
+        use_middle_indices_grid=False,
+        timestep_scale_multiplier = 1000.0,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs,
+    ):
         super().__init__()
         self.generator = None
         self.vae_scale_factors = vae_scale_factors
+        self.use_middle_indices_grid = use_middle_indices_grid
         self.dtype = dtype
-        self.out_channels = in_channels
+        self.in_channels = in_channels
-        self.inner_dim = num_attention_heads * attention_head_dim
+        self.cross_attention_dim = cross_attention_dim
+        self.attention_head_dim = attention_head_dim
+        self.num_attention_heads = num_attention_heads
+        self.caption_channels = caption_channels
+        self.num_layers = num_layers
+        self.positional_embedding_theta = positional_embedding_theta
+        self.positional_embedding_max_pos = positional_embedding_max_pos
+        self.split_positional_embedding = LTXRopeType.from_dict(kwargs)
+        self.freq_grid_generator = (
+            generate_freq_grid_np if LTXFrequenciesPrecision.from_dict(kwargs) == LTXFrequenciesPrecision.FLOAT64
+            else generate_freq_grid_pytorch
+        )
         self.causal_temporal_positioning = causal_temporal_positioning
+        self.operations = operations
+        self.timestep_scale_multiplier = timestep_scale_multiplier
 
-        self.patchify_proj = operations.Linear(in_channels, self.inner_dim, bias=True, dtype=dtype, device=device)
+        # Common dimensions
+        self.inner_dim = num_attention_heads * attention_head_dim
+        self.out_channels = in_channels
+
+        # Initialize common components
+        self._init_common_components(device, dtype)
+
+        # Initialize model-specific components
+        self._init_model_components(device, dtype, **kwargs)
+
+        # Initialize transformer blocks
+        self._init_transformer_blocks(device, dtype, **kwargs)
+
+        # Initialize output components
+        self._init_output_components(device, dtype)
+
+    def _init_common_components(self, device, dtype):
+        """Initialize components common to all LTX models
+        - patchify_proj: Linear projection for patchifying input
+        - adaln_single: AdaLN layer for timestep embedding
+        - caption_projection: Linear projection for caption embedding
+        """
+        self.patchify_proj = self.operations.Linear(
+            self.in_channels, self.inner_dim, bias=True, dtype=dtype, device=device
+        )
 
         self.adaln_single = AdaLayerNormSingle(
-            self.inner_dim, use_additional_conditions=False, dtype=dtype, device=device, operations=operations
+            self.inner_dim, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations
         )
 
-        # self.adaln_single.linear = operations.Linear(self.inner_dim, 4 * self.inner_dim, bias=True, dtype=dtype, device=device)
-
         self.caption_projection = PixArtAlphaTextProjection(
-            in_features=caption_channels, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=operations
+            in_features=self.caption_channels,
+            hidden_size=self.inner_dim,
+            dtype=dtype,
+            device=device,
+            operations=self.operations,
         )
 
+    @abstractmethod
+    def _init_model_components(self, device, dtype, **kwargs):
+        """Initialize model-specific components. Must be implemented by subclasses."""
+        pass
+
+    @abstractmethod
+    def _init_transformer_blocks(self, device, dtype, **kwargs):
+        """Initialize transformer blocks. Must be implemented by subclasses."""
+        pass
+
+    @abstractmethod
+    def _init_output_components(self, device, dtype):
+        """Initialize output components. Must be implemented by subclasses."""
+        pass
+
+    @abstractmethod
+    def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
+        """Process input data. Must be implemented by subclasses."""
+        pass
+
+    @abstractmethod
+    def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, **kwargs):
+        """Process transformer blocks. Must be implemented by subclasses."""
+        pass
+
+    @abstractmethod
+    def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):
+        """Process output data. Must be implemented by subclasses."""
+        pass
+
+    def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwargs):
+        """Prepare timestep embeddings."""
+        grid_mask = kwargs.get("grid_mask", None)
+        if grid_mask is not None:
+            timestep = timestep[:, grid_mask]
+
+        timestep = timestep * self.timestep_scale_multiplier
+        timestep, embedded_timestep = self.adaln_single(
+            timestep.flatten(),
+            {"resolution": None, "aspect_ratio": None},
+            batch_size=batch_size,
+            hidden_dtype=hidden_dtype,
+        )
+
+        # Second dimension is 1 or number of tokens (if timestep_per_token)
+        timestep = timestep.view(batch_size, -1, timestep.shape[-1])
+        embedded_timestep = embedded_timestep.view(batch_size, -1, embedded_timestep.shape[-1])
+
+        return timestep, embedded_timestep
+
+    def _prepare_context(self, context, batch_size, x, attention_mask=None):
+        """Prepare context for transformer blocks."""
+        if self.caption_projection is not None:
+            context = self.caption_projection(context)
+            context = context.view(batch_size, -1, x.shape[-1])
+
+        return context, attention_mask
+
+    def _precompute_freqs_cis(
+        self,
+        indices_grid,
+        dim,
+        out_dtype,
+        theta=10000.0,
+        max_pos=[20, 2048, 2048],
+        use_middle_indices_grid=False,
+        num_attention_heads=32,
+    ):
+        split_mode = self.split_positional_embedding == LTXRopeType.SPLIT
+        indices = self.freq_grid_generator(theta, indices_grid.shape[1], dim, indices_grid.device)
+        freqs = generate_freqs(indices, indices_grid, max_pos, use_middle_indices_grid)
+
+        if split_mode:
+            expected_freqs = dim // 2
+            current_freqs = freqs.shape[-1]
+            pad_size = expected_freqs - current_freqs
+            cos_freq, sin_freq = split_freqs_cis(freqs, pad_size, num_attention_heads)
+        else:
+            # 2 because of cos and sin by 3 for (t, x, y), 1 for temporal only
+            n_elem = 2 * indices_grid.shape[1]
+            cos_freq, sin_freq = interleaved_freqs_cis(freqs, dim % n_elem)
+        return cos_freq.to(out_dtype), sin_freq.to(out_dtype), split_mode
+
+    def _prepare_positional_embeddings(self, pixel_coords, frame_rate, x_dtype):
+        """Prepare positional embeddings."""
+        fractional_coords = pixel_coords.to(torch.float32)
+        fractional_coords[:, 0] = fractional_coords[:, 0] * (1.0 / frame_rate)
+        pe = self._precompute_freqs_cis(
+            fractional_coords,
+            dim=self.inner_dim,
+            out_dtype=x_dtype,
+            max_pos=self.positional_embedding_max_pos,
+            use_middle_indices_grid=self.use_middle_indices_grid,
+            num_attention_heads=self.num_attention_heads,
+        )
+        return pe
+
+    def _prepare_attention_mask(self, attention_mask, x_dtype):
+        """Prepare attention mask."""
+        if attention_mask is not None and not torch.is_floating_point(attention_mask):
+            attention_mask = (attention_mask - 1).to(x_dtype).reshape(
+                (attention_mask.shape[0], 1, -1, attention_mask.shape[-1])
+            ) * torch.finfo(x_dtype).max
+        return attention_mask
+
+    def forward(
+        self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs
+    ):
+        """
+        Forward pass for LTX models.
+
+        Args:
+            x: Input tensor
+            timestep: Timestep tensor
+            context: Context tensor (e.g., text embeddings)
+            attention_mask: Attention mask tensor
+            frame_rate: Frame rate for temporal processing
+            transformer_options: Additional options for transformer blocks
+            keyframe_idxs: Keyframe indices for temporal processing
+            **kwargs: Additional keyword arguments
+
+        Returns:
+            Processed output tensor
+        """
+        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, timestep, context, attention_mask, frame_rate, transformer_options, keyframe_idxs, denoise_mask=denoise_mask, **kwargs)
+
+    def _forward(
+        self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs
+    ):
+        """
+        Internal forward pass for LTX models.
+
+        Args:
+            x: Input tensor
+            timestep: Timestep tensor
+            context: Context tensor (e.g., text embeddings)
+            attention_mask: Attention mask tensor
+            frame_rate: Frame rate for temporal processing
+            transformer_options: Additional options for transformer blocks
+            keyframe_idxs: Keyframe indices for temporal processing
+            **kwargs: Additional keyword arguments
+
+        Returns:
+            Processed output tensor
+        """
+        if isinstance(x, list):
+            input_dtype = x[0].dtype
+            batch_size = x[0].shape[0]
+        else:
+            input_dtype = x.dtype
+            batch_size = x.shape[0]
+        # Process input
+        merged_args = {**transformer_options, **kwargs}
+        x, pixel_coords, additional_args = self._process_input(x, keyframe_idxs, denoise_mask, **merged_args)
+        merged_args.update(additional_args)
+
+        # Prepare timestep and context
+        timestep, embedded_timestep = self._prepare_timestep(timestep, batch_size, input_dtype, **merged_args)
+        context, attention_mask = self._prepare_context(context, batch_size, x, attention_mask)
+
+        # Prepare attention mask and positional embeddings
+        attention_mask = self._prepare_attention_mask(attention_mask, input_dtype)
+        pe = self._prepare_positional_embeddings(pixel_coords, frame_rate, input_dtype)
+
+        # Process transformer blocks
+        x = self._process_transformer_blocks(
+            x, context, attention_mask, timestep, pe, transformer_options=transformer_options, **merged_args
+        )
+
+        # Process output
+        x = self._process_output(x, embedded_timestep, keyframe_idxs, **merged_args)
+        return x
+
+
+class LTXVModel(LTXBaseModel):
+    """LTXV model for video generation."""
+
+    def __init__(
+        self,
+        in_channels=128,
+        cross_attention_dim=2048,
+        attention_head_dim=64,
+        num_attention_heads=32,
+        caption_channels=4096,
+        num_layers=28,
+        positional_embedding_theta=10000.0,
+        positional_embedding_max_pos=[20, 2048, 2048],
+        causal_temporal_positioning=False,
+        vae_scale_factors=(8, 32, 32),
+        use_middle_indices_grid=False,
+        timestep_scale_multiplier = 1000.0,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs,
+    ):
+        super().__init__(
+            in_channels=in_channels,
+            cross_attention_dim=cross_attention_dim,
+            attention_head_dim=attention_head_dim,
+            num_attention_heads=num_attention_heads,
+            caption_channels=caption_channels,
+            num_layers=num_layers,
+            positional_embedding_theta=positional_embedding_theta,
+            positional_embedding_max_pos=positional_embedding_max_pos,
+            causal_temporal_positioning=causal_temporal_positioning,
+            vae_scale_factors=vae_scale_factors,
+            use_middle_indices_grid=use_middle_indices_grid,
+            timestep_scale_multiplier=timestep_scale_multiplier,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+            **kwargs,
+        )
+
+    def _init_model_components(self, device, dtype, **kwargs):
+        """Initialize LTXV-specific components."""
+        # No additional components needed for LTXV beyond base class
+        pass
+
+    def _init_transformer_blocks(self, device, dtype, **kwargs):
+        """Initialize transformer blocks for LTXV."""
         self.transformer_blocks = nn.ModuleList(
             [
                 BasicTransformerBlock(
                     self.inner_dim,
-                    num_attention_heads,
+                    self.num_attention_heads,
-                    attention_head_dim,
+                    self.attention_head_dim,
-                    context_dim=cross_attention_dim,
+                    context_dim=self.cross_attention_dim,
-                    # attn_precision=attn_precision,
+                    dtype=dtype,
-                    dtype=dtype, device=device, operations=operations
+                    device=device,
+                    operations=self.operations,
                 )
-                for d in range(num_layers)
+                for _ in range(self.num_layers)
             ]
         )
 
+    def _init_output_components(self, device, dtype):
+        """Initialize output components for LTXV."""
         self.scale_shift_table = nn.Parameter(torch.empty(2, self.inner_dim, dtype=dtype, device=device))
-        self.norm_out = operations.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.norm_out = self.operations.LayerNorm(
-        self.proj_out = operations.Linear(self.inner_dim, self.out_channels, dtype=dtype, device=device)
+            self.inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device
-
+        )
-        self.patchifier = SymmetricPatchifier(1)
+        self.proj_out = self.operations.Linear(self.inner_dim, self.out_channels, dtype=dtype, device=device)
-
+        self.patchifier = SymmetricPatchifier(1, start_end=True)
-    def forward(self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, **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, timestep, context, attention_mask, frame_rate, transformer_options, keyframe_idxs, **kwargs)
-
-    def _forward(self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, **kwargs):
-        patches_replace = transformer_options.get("patches_replace", {})
-
-        orig_shape = list(x.shape)
 
+    def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
+        """Process input for LTXV."""
+        additional_args = {"orig_shape": list(x.shape)}
         x, latent_coords = self.patchifier.patchify(x)
         pixel_coords = latent_to_pixel_coords(
             latent_coords=latent_coords,
@@ -423,44 +880,30 @@ class LTXVModel(torch.nn.Module):
             causal_fix=self.causal_temporal_positioning,
         )
 
+        grid_mask = None
         if keyframe_idxs is not None:
-            pixel_coords[:, :, -keyframe_idxs.shape[2]:] = keyframe_idxs
+            additional_args.update({ "orig_patchified_shape": list(x.shape)})
+            denoise_mask = self.patchifier.patchify(denoise_mask)[0]
+            grid_mask = ~torch.any(denoise_mask < 0, dim=-1)[0]
+            additional_args.update({"grid_mask": grid_mask})
+            x = x[:, grid_mask, :]
+            pixel_coords = pixel_coords[:, :, grid_mask, ...]
 
-        fractional_coords = pixel_coords.to(torch.float32)
+            kf_grid_mask = grid_mask[-keyframe_idxs.shape[2]:]
-        fractional_coords[:, 0] = fractional_coords[:, 0] * (1.0 / frame_rate)
+            keyframe_idxs = keyframe_idxs[..., kf_grid_mask, :]
+            pixel_coords[:, :, -keyframe_idxs.shape[2]:, :] = keyframe_idxs
 
         x = self.patchify_proj(x)
-        timestep = timestep * 1000.0
+        return x, pixel_coords, additional_args
-
-        if attention_mask is not None and not torch.is_floating_point(attention_mask):
-            attention_mask = (attention_mask - 1).to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) * torch.finfo(x.dtype).max
-
-        pe = precompute_freqs_cis(fractional_coords, dim=self.inner_dim, out_dtype=x.dtype)
-
-        batch_size = x.shape[0]
-        timestep, embedded_timestep = self.adaln_single(
-            timestep.flatten(),
-            {"resolution": None, "aspect_ratio": None},
-            batch_size=batch_size,
-            hidden_dtype=x.dtype,
-        )
-        # Second dimension is 1 or number of tokens (if timestep_per_token)
-        timestep = timestep.view(batch_size, -1, timestep.shape[-1])
-        embedded_timestep = embedded_timestep.view(
-            batch_size, -1, embedded_timestep.shape[-1]
-        )
-
-        # 2. Blocks
-        if self.caption_projection is not None:
-            batch_size = x.shape[0]
-            context = self.caption_projection(context)
-            context = context.view(
-                batch_size, -1, x.shape[-1]
-            )
 
+    def _process_transformer_blocks(self, x, context, attention_mask, timestep, pe, transformer_options={}, **kwargs):
+        """Process transformer blocks for LTXV."""
+        patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
+
         for i, block in enumerate(self.transformer_blocks):
             if ("double_block", i) in blocks_replace:
+
                 def block_wrap(args):
                     out = {}
                     out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"])
@@ -478,16 +921,28 @@ class LTXVModel(torch.nn.Module):
                     transformer_options=transformer_options,
                 )
 
-        # 3. Output
+        return x
+
+    def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):
+        """Process output for LTXV."""
+        # Apply scale-shift modulation
         scale_shift_values = (
             self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + embedded_timestep[:, :, None]
         )
         shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]
+
         x = self.norm_out(x)
-        # Modulation
+        x = x * (1 + scale) + shift
-        x = torch.addcmul(x, x, scale).add_(shift)
         x = self.proj_out(x)
 
+        if keyframe_idxs is not None:
+            grid_mask = kwargs["grid_mask"]
+            orig_patchified_shape = kwargs["orig_patchified_shape"]
+            full_x = torch.zeros(orig_patchified_shape, dtype=x.dtype, device=x.device)
+            full_x[:, grid_mask, :] = x
+            x = full_x
+        # Unpatchify to restore original dimensions
+        orig_shape = kwargs["orig_shape"]
         x = self.patchifier.unpatchify(
             latents=x,
             output_height=orig_shape[3],

comfy/ldm/lightricks/symmetric_patchifier.py

@@ -21,20 +21,23 @@ def latent_to_pixel_coords(
     Returns:
         Tensor: A tensor of pixel coordinates corresponding to the input latent coordinates.
     """
+    shape = [1] * latent_coords.ndim
+    shape[1] = -1
     pixel_coords = (
         latent_coords
-        * torch.tensor(scale_factors, device=latent_coords.device)[None, :, None]
+        * torch.tensor(scale_factors, device=latent_coords.device).view(*shape)
     )
     if causal_fix:
         # Fix temporal scale for first frame to 1 due to causality
-        pixel_coords[:, 0] = (pixel_coords[:, 0] + 1 - scale_factors[0]).clamp(min=0)
+        pixel_coords[:, 0, ...] = (pixel_coords[:, 0, ...] + 1 - scale_factors[0]).clamp(min=0)
     return pixel_coords
 
 
 class Patchifier(ABC):
-    def __init__(self, patch_size: int):
+    def __init__(self, patch_size: int, start_end: bool=False):
         super().__init__()
         self._patch_size = (1, patch_size, patch_size)
+        self.start_end = start_end
 
     @abstractmethod
     def patchify(
@@ -71,11 +74,23 @@ class Patchifier(ABC):
             torch.arange(0, latent_width, self._patch_size[2], device=device),
             indexing="ij",
         )
-        latent_sample_coords = torch.stack(latent_sample_coords, dim=0)
+        latent_sample_coords_start = torch.stack(latent_sample_coords, dim=0)
-        latent_coords = latent_sample_coords.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
+        delta = torch.tensor(self._patch_size, device=latent_sample_coords_start.device, dtype=latent_sample_coords_start.dtype)[:, None, None, None]
-        latent_coords = rearrange(
+        latent_sample_coords_end = latent_sample_coords_start + delta
-            latent_coords, "b c f h w -> b c (f h w)", b=batch_size
+
+        latent_sample_coords_start = latent_sample_coords_start.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
+        latent_sample_coords_start = rearrange(
+            latent_sample_coords_start, "b c f h w -> b c (f h w)", b=batch_size
         )
+        if self.start_end:
+            latent_sample_coords_end = latent_sample_coords_end.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
+            latent_sample_coords_end = rearrange(
+                latent_sample_coords_end, "b c f h w -> b c (f h w)", b=batch_size
+            )
+
+            latent_coords = torch.stack((latent_sample_coords_start, latent_sample_coords_end), dim=-1)
+        else:
+            latent_coords = latent_sample_coords_start
         return latent_coords
 
 
@@ -115,3 +130,61 @@ class SymmetricPatchifier(Patchifier):
             q=self._patch_size[2],
         )
         return latents
+
+
+class AudioPatchifier(Patchifier):
+    def __init__(self, patch_size: int,
+        sample_rate=16000,
+        hop_length=160,
+        audio_latent_downsample_factor=4,
+        is_causal=True,
+        start_end=False,
+        shift = 0
+    ):
+        super().__init__(patch_size, start_end=start_end)
+        self.hop_length = hop_length
+        self.sample_rate = sample_rate
+        self.audio_latent_downsample_factor = audio_latent_downsample_factor
+        self.is_causal = is_causal
+        self.shift = shift
+
+    def copy_with_shift(self, shift):
+        return AudioPatchifier(
+            self.patch_size, self.sample_rate, self.hop_length, self.audio_latent_downsample_factor,
+            self.is_causal, self.start_end, shift
+        )
+
+    def _get_audio_latent_time_in_sec(self, start_latent, end_latent: int, dtype: torch.dtype, device=torch.device):
+        audio_latent_frame = torch.arange(start_latent, end_latent, dtype=dtype, device=device)
+        audio_mel_frame = audio_latent_frame * self.audio_latent_downsample_factor
+        if self.is_causal:
+            audio_mel_frame = (audio_mel_frame + 1 - self.audio_latent_downsample_factor).clip(min=0)
+        return audio_mel_frame * self.hop_length / self.sample_rate
+
+
+    def patchify(self, audio_latents: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        # audio_latents: (batch, channels, time, freq)
+        b, _, t, _ = audio_latents.shape
+        audio_latents = rearrange(
+            audio_latents,
+            "b c t f -> b t (c f)",
+        )
+
+        audio_latents_start_timings = self._get_audio_latent_time_in_sec(self.shift, t + self.shift, torch.float32, audio_latents.device)
+        audio_latents_start_timings = audio_latents_start_timings.unsqueeze(0).expand(b, -1).unsqueeze(1)
+
+        if self.start_end:
+            audio_latents_end_timings = self._get_audio_latent_time_in_sec(self.shift + 1, t + self.shift + 1, torch.float32, audio_latents.device)
+            audio_latents_end_timings = audio_latents_end_timings.unsqueeze(0).expand(b, -1).unsqueeze(1)
+
+            audio_latents_timings = torch.stack([audio_latents_start_timings, audio_latents_end_timings], dim=-1)
+        else:
+            audio_latents_timings = audio_latents_start_timings
+        return audio_latents, audio_latents_timings
+
+    def unpatchify(self, audio_latents: torch.Tensor, channels: int, freq: int) -> torch.Tensor:
+        # audio_latents: (batch, time, freq * channels)
+        audio_latents = rearrange(
+            audio_latents, "b t (c f) -> b c t f", c=channels, f=freq
+        )
+        return audio_latents

comfy/ldm/lightricks/vae/audio_vae.py

@@ -0,0 +1,286 @@
+import json
+from dataclasses import dataclass
+import math
+import torch
+import torchaudio
+
+import comfy.model_management
+import comfy.model_patcher
+import comfy.utils as utils
+from comfy.ldm.mmaudio.vae.distributions import DiagonalGaussianDistribution
+from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier
+from comfy.ldm.lightricks.vae.causal_audio_autoencoder import (
+    CausalityAxis,
+    CausalAudioAutoencoder,
+)
+from comfy.ldm.lightricks.vocoders.vocoder import Vocoder
+
+LATENT_DOWNSAMPLE_FACTOR = 4
+
+
+@dataclass(frozen=True)
+class AudioVAEComponentConfig:
+    """Container for model component configuration extracted from metadata."""
+
+    autoencoder: dict
+    vocoder: dict
+
+    @classmethod
+    def from_metadata(cls, metadata: dict) -> "AudioVAEComponentConfig":
+        assert metadata is not None and "config" in metadata, "Metadata is required for audio VAE"
+
+        raw_config = metadata["config"]
+        if isinstance(raw_config, str):
+            parsed_config = json.loads(raw_config)
+        else:
+            parsed_config = raw_config
+
+        audio_config = parsed_config.get("audio_vae")
+        vocoder_config = parsed_config.get("vocoder")
+
+        assert audio_config is not None, "Audio VAE config is required for audio VAE"
+        assert vocoder_config is not None, "Vocoder config is required for audio VAE"
+
+        return cls(autoencoder=audio_config, vocoder=vocoder_config)
+
+
+class ModelDeviceManager:
+    """Manages device placement and GPU residency for the composed model."""
+
+    def __init__(self, module: torch.nn.Module):
+        load_device = comfy.model_management.get_torch_device()
+        offload_device = comfy.model_management.vae_offload_device()
+        self.patcher = comfy.model_patcher.ModelPatcher(module, load_device, offload_device)
+
+    def ensure_model_loaded(self) -> None:
+        comfy.model_management.free_memory(
+            self.patcher.model_size(),
+            self.patcher.load_device,
+        )
+        comfy.model_management.load_model_gpu(self.patcher)
+
+    def move_to_load_device(self, tensor: torch.Tensor) -> torch.Tensor:
+        return tensor.to(self.patcher.load_device)
+
+    @property
+    def load_device(self):
+        return self.patcher.load_device
+
+
+class AudioLatentNormalizer:
+    """Applies per-channel statistics in patch space and restores original layout."""
+
+    def __init__(self, patchfier: AudioPatchifier, statistics_processor: torch.nn.Module):
+        self.patchifier = patchfier
+        self.statistics = statistics_processor
+
+    def normalize(self, latents: torch.Tensor) -> torch.Tensor:
+        channels = latents.shape[1]
+        freq = latents.shape[3]
+        patched, _ = self.patchifier.patchify(latents)
+        normalized = self.statistics.normalize(patched)
+        return self.patchifier.unpatchify(normalized, channels=channels, freq=freq)
+
+    def denormalize(self, latents: torch.Tensor) -> torch.Tensor:
+        channels = latents.shape[1]
+        freq = latents.shape[3]
+        patched, _ = self.patchifier.patchify(latents)
+        denormalized = self.statistics.un_normalize(patched)
+        return self.patchifier.unpatchify(denormalized, channels=channels, freq=freq)
+
+
+class AudioPreprocessor:
+    """Prepares raw waveforms for the autoencoder by matching training conditions."""
+
+    def __init__(self, target_sample_rate: int, mel_bins: int, mel_hop_length: int, n_fft: int):
+        self.target_sample_rate = target_sample_rate
+        self.mel_bins = mel_bins
+        self.mel_hop_length = mel_hop_length
+        self.n_fft = n_fft
+
+    def resample(self, waveform: torch.Tensor, source_rate: int) -> torch.Tensor:
+        if source_rate == self.target_sample_rate:
+            return waveform
+        return torchaudio.functional.resample(waveform, source_rate, self.target_sample_rate)
+
+    @staticmethod
+    def normalize_amplitude(
+        waveform: torch.Tensor, max_amplitude: float = 0.5, eps: float = 1e-5
+    ) -> torch.Tensor:
+        waveform = waveform - waveform.mean(dim=2, keepdim=True)
+        peak = torch.max(torch.abs(waveform)) + eps
+        scale = peak.clamp(max=max_amplitude) / peak
+        return waveform * scale
+
+    def waveform_to_mel(
+        self, waveform: torch.Tensor, waveform_sample_rate: int, device
+    ) -> torch.Tensor:
+        waveform = self.resample(waveform, waveform_sample_rate)
+        waveform = self.normalize_amplitude(waveform)
+
+        mel_transform = torchaudio.transforms.MelSpectrogram(
+            sample_rate=self.target_sample_rate,
+            n_fft=self.n_fft,
+            win_length=self.n_fft,
+            hop_length=self.mel_hop_length,
+            f_min=0.0,
+            f_max=self.target_sample_rate / 2.0,
+            n_mels=self.mel_bins,
+            window_fn=torch.hann_window,
+            center=True,
+            pad_mode="reflect",
+            power=1.0,
+            mel_scale="slaney",
+            norm="slaney",
+        ).to(device)
+
+        mel = mel_transform(waveform)
+        mel = torch.log(torch.clamp(mel, min=1e-5))
+        return mel.permute(0, 1, 3, 2).contiguous()
+
+
+class AudioVAE(torch.nn.Module):
+    """High-level Audio VAE wrapper exposing encode and decode entry points."""
+
+    def __init__(self, state_dict: dict, metadata: dict):
+        super().__init__()
+
+        component_config = AudioVAEComponentConfig.from_metadata(metadata)
+
+        vae_sd = utils.state_dict_prefix_replace(state_dict, {"audio_vae.": ""}, filter_keys=True)
+        vocoder_sd = utils.state_dict_prefix_replace(state_dict, {"vocoder.": ""}, filter_keys=True)
+
+        self.autoencoder = CausalAudioAutoencoder(config=component_config.autoencoder)
+        self.vocoder = Vocoder(config=component_config.vocoder)
+
+        self.autoencoder.load_state_dict(vae_sd, strict=False)
+        self.vocoder.load_state_dict(vocoder_sd, strict=False)
+
+        autoencoder_config = self.autoencoder.get_config()
+        self.normalizer = AudioLatentNormalizer(
+            AudioPatchifier(
+                patch_size=1,
+                audio_latent_downsample_factor=LATENT_DOWNSAMPLE_FACTOR,
+                sample_rate=autoencoder_config["sampling_rate"],
+                hop_length=autoencoder_config["mel_hop_length"],
+                is_causal=autoencoder_config["is_causal"],
+            ),
+            self.autoencoder.per_channel_statistics,
+        )
+
+        self.preprocessor = AudioPreprocessor(
+            target_sample_rate=autoencoder_config["sampling_rate"],
+            mel_bins=autoencoder_config["mel_bins"],
+            mel_hop_length=autoencoder_config["mel_hop_length"],
+            n_fft=autoencoder_config["n_fft"],
+        )
+
+        self.device_manager = ModelDeviceManager(self)
+
+    def encode(self, audio: dict) -> torch.Tensor:
+        """Encode a waveform dictionary into normalized latent tensors."""
+
+        waveform = audio["waveform"]
+        waveform_sample_rate = audio["sample_rate"]
+        input_device = waveform.device
+        # Ensure that Audio VAE is loaded on the correct device.
+        self.device_manager.ensure_model_loaded()
+
+        waveform = self.device_manager.move_to_load_device(waveform)
+        expected_channels = self.autoencoder.encoder.in_channels
+        if waveform.shape[1] != expected_channels:
+            raise ValueError(
+                f"Input audio must have {expected_channels} channels, got {waveform.shape[1]}"
+            )
+
+        mel_spec = self.preprocessor.waveform_to_mel(
+            waveform, waveform_sample_rate, device=self.device_manager.load_device
+        )
+
+        latents = self.autoencoder.encode(mel_spec)
+        posterior = DiagonalGaussianDistribution(latents)
+        latent_mode = posterior.mode()
+
+        normalized = self.normalizer.normalize(latent_mode)
+        return normalized.to(input_device)
+
+    def decode(self, latents: torch.Tensor) -> torch.Tensor:
+        """Decode normalized latent tensors into an audio waveform."""
+        original_shape = latents.shape
+
+        # Ensure that Audio VAE is loaded on the correct device.
+        self.device_manager.ensure_model_loaded()
+
+        latents = self.device_manager.move_to_load_device(latents)
+        latents = self.normalizer.denormalize(latents)
+
+        target_shape = self.target_shape_from_latents(original_shape)
+        mel_spec = self.autoencoder.decode(latents, target_shape=target_shape)
+
+        waveform = self.run_vocoder(mel_spec)
+        return self.device_manager.move_to_load_device(waveform)
+
+    def target_shape_from_latents(self, latents_shape):
+        batch, _, time, _ = latents_shape
+        target_length = time * LATENT_DOWNSAMPLE_FACTOR
+        if self.autoencoder.causality_axis != CausalityAxis.NONE:
+            target_length -= LATENT_DOWNSAMPLE_FACTOR - 1
+        return (
+            batch,
+            self.autoencoder.decoder.out_ch,
+            target_length,
+            self.autoencoder.mel_bins,
+        )
+
+    def num_of_latents_from_frames(self, frames_number: int, frame_rate: int) -> int:
+        return math.ceil((float(frames_number) / frame_rate) * self.latents_per_second)
+
+    def run_vocoder(self, mel_spec: torch.Tensor) -> torch.Tensor:
+        audio_channels = self.autoencoder.decoder.out_ch
+        vocoder_input = mel_spec.transpose(2, 3)
+
+        if audio_channels == 1:
+            vocoder_input = vocoder_input.squeeze(1)
+        elif audio_channels != 2:
+            raise ValueError(f"Unsupported audio_channels: {audio_channels}")
+
+        return self.vocoder(vocoder_input)
+
+    @property
+    def sample_rate(self) -> int:
+        return int(self.autoencoder.sampling_rate)
+
+    @property
+    def mel_hop_length(self) -> int:
+        return int(self.autoencoder.mel_hop_length)
+
+    @property
+    def mel_bins(self) -> int:
+        return int(self.autoencoder.mel_bins)
+
+    @property
+    def latent_channels(self) -> int:
+        return int(self.autoencoder.decoder.z_channels)
+
+    @property
+    def latent_frequency_bins(self) -> int:
+        return int(self.mel_bins // LATENT_DOWNSAMPLE_FACTOR)
+
+    @property
+    def latents_per_second(self) -> float:
+        return self.sample_rate / self.mel_hop_length / LATENT_DOWNSAMPLE_FACTOR
+
+    @property
+    def output_sample_rate(self) -> int:
+        output_rate = getattr(self.vocoder, "output_sample_rate", None)
+        if output_rate is not None:
+            return int(output_rate)
+        upsample_factor = getattr(self.vocoder, "upsample_factor", None)
+        if upsample_factor is None:
+            raise AttributeError(
+                "Vocoder is missing upsample_factor; cannot infer output sample rate"
+            )
+        return int(self.sample_rate * upsample_factor / self.mel_hop_length)
+
+    def memory_required(self, input_shape):
+        return self.device_manager.patcher.model_size()

comfy/ldm/lightricks/vae/causal_audio_autoencoder.py

@@ -0,0 +1,909 @@
+from __future__ import annotations
+import torch
+from torch import nn
+from torch.nn import functional as F
+from typing import Optional
+from enum import Enum
+from .pixel_norm import PixelNorm
+import comfy.ops
+import logging
+
+ops = comfy.ops.disable_weight_init
+
+
+class StringConvertibleEnum(Enum):
+    """
+    Base enum class that provides string-to-enum conversion functionality.
+
+    This mixin adds a str_to_enum() class method that handles conversion from
+    strings, None, or existing enum instances with case-insensitive matching.
+    """
+
+    @classmethod
+    def str_to_enum(cls, value):
+        """
+        Convert a string, enum instance, or None to the appropriate enum member.
+
+        Args:
+            value: Can be an enum instance of this class, a string, or None
+
+        Returns:
+            Enum member of this class
+
+        Raises:
+            ValueError: If the value cannot be converted to a valid enum member
+        """
+        # Already an enum instance of this class
+        if isinstance(value, cls):
+            return value
+
+        # None maps to NONE member if it exists
+        if value is None:
+            if hasattr(cls, "NONE"):
+                return cls.NONE
+            raise ValueError(f"{cls.__name__} does not have a NONE member to map None to")
+
+        # String conversion (case-insensitive)
+        if isinstance(value, str):
+            value_lower = value.lower()
+
+            # Try to match against enum values
+            for member in cls:
+                # Handle members with None values
+                if member.value is None:
+                    if value_lower == "none":
+                        return member
+                # Handle members with string values
+                elif isinstance(member.value, str) and member.value.lower() == value_lower:
+                    return member
+
+            # Build helpful error message with valid values
+            valid_values = []
+            for member in cls:
+                if member.value is None:
+                    valid_values.append("none")
+                elif isinstance(member.value, str):
+                    valid_values.append(member.value)
+
+            raise ValueError(f"Invalid {cls.__name__} string: '{value}'. " f"Valid values are: {valid_values}")
+
+        raise ValueError(
+            f"Cannot convert type {type(value).__name__} to {cls.__name__} enum. "
+            f"Expected string, None, or {cls.__name__} instance."
+        )
+
+
+class AttentionType(StringConvertibleEnum):
+    """Enum for specifying the attention mechanism type."""
+
+    VANILLA = "vanilla"
+    LINEAR = "linear"
+    NONE = "none"
+
+
+class CausalityAxis(StringConvertibleEnum):
+    """Enum for specifying the causality axis in causal convolutions."""
+
+    NONE = None
+    WIDTH = "width"
+    HEIGHT = "height"
+    WIDTH_COMPATIBILITY = "width-compatibility"
+
+
+def Normalize(in_channels, *, num_groups=32, normtype="group"):
+    if normtype == "group":
+        return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+    elif normtype == "pixel":
+        return PixelNorm(dim=1, eps=1e-6)
+    else:
+        raise ValueError(f"Invalid normalization type: {normtype}")
+
+
+class CausalConv2d(nn.Module):
+    """
+    A causal 2D convolution.
+
+    This layer ensures that the output at time `t` only depends on inputs
+    at time `t` and earlier. It achieves this by applying asymmetric padding
+    to the time dimension (width) before the convolution.
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        dilation=1,
+        groups=1,
+        bias=True,
+        causality_axis: CausalityAxis = CausalityAxis.HEIGHT,
+    ):
+        super().__init__()
+
+        self.causality_axis = causality_axis
+
+        # Ensure kernel_size and dilation are tuples
+        kernel_size = nn.modules.utils._pair(kernel_size)
+        dilation = nn.modules.utils._pair(dilation)
+
+        # Calculate padding dimensions
+        pad_h = (kernel_size[0] - 1) * dilation[0]
+        pad_w = (kernel_size[1] - 1) * dilation[1]
+
+        # The padding tuple for F.pad is (pad_left, pad_right, pad_top, pad_bottom)
+        match self.causality_axis:
+            case CausalityAxis.NONE:
+                self.padding = (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2)
+            case CausalityAxis.WIDTH | CausalityAxis.WIDTH_COMPATIBILITY:
+                self.padding = (pad_w, 0, pad_h // 2, pad_h - pad_h // 2)
+            case CausalityAxis.HEIGHT:
+                self.padding = (pad_w // 2, pad_w - pad_w // 2, pad_h, 0)
+            case _:
+                raise ValueError(f"Invalid causality_axis: {causality_axis}")
+
+        # The internal convolution layer uses no padding, as we handle it manually
+        self.conv = ops.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=0,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+        )
+
+    def forward(self, x):
+        # Apply causal padding before convolution
+        x = F.pad(x, self.padding)
+        return self.conv(x)
+
+
+def make_conv2d(
+    in_channels,
+    out_channels,
+    kernel_size,
+    stride=1,
+    padding=None,
+    dilation=1,
+    groups=1,
+    bias=True,
+    causality_axis: Optional[CausalityAxis] = None,
+):
+    """
+    Create a 2D convolution layer that can be either causal or non-causal.
+
+    Args:
+        in_channels: Number of input channels
+        out_channels: Number of output channels
+        kernel_size: Size of the convolution kernel
+        stride: Convolution stride
+        padding: Padding (if None, will be calculated based on causal flag)
+        dilation: Dilation rate
+        groups: Number of groups for grouped convolution
+        bias: Whether to use bias
+        causality_axis: Dimension along which to apply causality.
+
+    Returns:
+        Either a regular Conv2d or CausalConv2d layer
+    """
+    if causality_axis is not None:
+        # For causal convolution, padding is handled internally by CausalConv2d
+        return CausalConv2d(in_channels, out_channels, kernel_size, stride, dilation, groups, bias, causality_axis)
+    else:
+        # For non-causal convolution, use symmetric padding if not specified
+        if padding is None:
+            if isinstance(kernel_size, int):
+                padding = kernel_size // 2
+            else:
+                padding = tuple(k // 2 for k in kernel_size)
+        return ops.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+        )
+
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels, with_conv, causality_axis: CausalityAxis = CausalityAxis.HEIGHT):
+        super().__init__()
+        self.with_conv = with_conv
+        self.causality_axis = causality_axis
+        if self.with_conv:
+            self.conv = make_conv2d(in_channels, in_channels, kernel_size=3, stride=1, causality_axis=causality_axis)
+
+    def forward(self, x):
+        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        if self.with_conv:
+            x = self.conv(x)
+            # Drop FIRST element in the causal axis to undo encoder's padding, while keeping the length 1 + 2 * n.
+            # For example, if the input is [0, 1, 2], after interpolation, the output is [0, 0, 1, 1, 2, 2].
+            # The causal convolution will pad the first element as [-, -, 0, 0, 1, 1, 2, 2],
+            # So the output elements rely on the following windows:
+            # 0: [-,-,0]
+            # 1: [-,0,0]
+            # 2: [0,0,1]
+            # 3: [0,1,1]
+            # 4: [1,1,2]
+            # 5: [1,2,2]
+            # Notice that the first and second elements in the output rely only on the first element in the input,
+            # while all other elements rely on two elements in the input.
+            # So we can drop the first element to undo the padding (rather than the last element).
+            # This is a no-op for non-causal convolutions.
+            match self.causality_axis:
+                case CausalityAxis.NONE:
+                    pass  # x remains unchanged
+                case CausalityAxis.HEIGHT:
+                    x = x[:, :, 1:, :]
+                case CausalityAxis.WIDTH:
+                    x = x[:, :, :, 1:]
+                case CausalityAxis.WIDTH_COMPATIBILITY:
+                    pass  # x remains unchanged
+                case _:
+                    raise ValueError(f"Invalid causality_axis: {self.causality_axis}")
+
+        return x
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer that can use either a strided convolution
+    or average pooling. Supports standard and causal padding for the
+    convolutional mode.
+    """
+
+    def __init__(self, in_channels, with_conv, causality_axis: CausalityAxis = CausalityAxis.WIDTH):
+        super().__init__()
+        self.with_conv = with_conv
+        self.causality_axis = causality_axis
+
+        if self.causality_axis != CausalityAxis.NONE and not self.with_conv:
+            raise ValueError("causality is only supported when `with_conv=True`.")
+
+        if self.with_conv:
+            # Do time downsampling here
+            # no asymmetric padding in torch conv, must do it ourselves
+            self.conv = ops.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
+
+    def forward(self, x):
+        if self.with_conv:
+            # (pad_left, pad_right, pad_top, pad_bottom)
+            match self.causality_axis:
+                case CausalityAxis.NONE:
+                    pad = (0, 1, 0, 1)
+                case CausalityAxis.WIDTH:
+                    pad = (2, 0, 0, 1)
+                case CausalityAxis.HEIGHT:
+                    pad = (0, 1, 2, 0)
+                case CausalityAxis.WIDTH_COMPATIBILITY:
+                    pad = (1, 0, 0, 1)
+                case _:
+                    raise ValueError(f"Invalid causality_axis: {self.causality_axis}")
+
+            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+            x = self.conv(x)
+        else:
+            # This branch is only taken if with_conv=False, which implies causality_axis is NONE.
+            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
+
+        return x
+
+
+class ResnetBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels,
+        out_channels=None,
+        conv_shortcut=False,
+        dropout,
+        temb_channels=512,
+        norm_type="group",
+        causality_axis: CausalityAxis = CausalityAxis.HEIGHT,
+    ):
+        super().__init__()
+        self.causality_axis = causality_axis
+
+        if self.causality_axis != CausalityAxis.NONE and norm_type == "group":
+            raise ValueError("Causal ResnetBlock with GroupNorm is not supported.")
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+        self.use_conv_shortcut = conv_shortcut
+
+        self.norm1 = Normalize(in_channels, normtype=norm_type)
+        self.non_linearity = nn.SiLU()
+        self.conv1 = make_conv2d(in_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis)
+        if temb_channels > 0:
+            self.temb_proj = ops.Linear(temb_channels, out_channels)
+        self.norm2 = Normalize(out_channels, normtype=norm_type)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = make_conv2d(out_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis)
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = make_conv2d(
+                    in_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis
+                )
+            else:
+                self.nin_shortcut = make_conv2d(
+                    in_channels, out_channels, kernel_size=1, stride=1, causality_axis=causality_axis
+                )
+
+    def forward(self, x, temb):
+        h = x
+        h = self.norm1(h)
+        h = self.non_linearity(h)
+        h = self.conv1(h)
+
+        if temb is not None:
+            h = h + self.temb_proj(self.non_linearity(temb))[:, :, None, None]
+
+        h = self.norm2(h)
+        h = self.non_linearity(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                x = self.conv_shortcut(x)
+            else:
+                x = self.nin_shortcut(x)
+
+        return x + h
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels, norm_type="group"):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels, normtype=norm_type)
+        self.q = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.k = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.v = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.proj_out = ops.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, h, w = q.shape
+        q = q.reshape(b, c, h * w).contiguous()
+        q = q.permute(0, 2, 1).contiguous()  # b,hw,c
+        k = k.reshape(b, c, h * w).contiguous()  # b,c,hw
+        w_ = torch.bmm(q, k).contiguous()  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+        w_ = w_ * (int(c) ** (-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = v.reshape(b, c, h * w).contiguous()
+        w_ = w_.permute(0, 2, 1).contiguous()  # b,hw,hw (first hw of k, second of q)
+        h_ = torch.bmm(v, w_).contiguous()  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+        h_ = h_.reshape(b, c, h, w).contiguous()
+
+        h_ = self.proj_out(h_)
+
+        return x + h_
+
+
+def make_attn(in_channels, attn_type="vanilla", norm_type="group"):
+    # Convert string to enum if needed
+    attn_type = AttentionType.str_to_enum(attn_type)
+
+    if attn_type != AttentionType.NONE:
+        logging.info(f"making attention of type '{attn_type.value}' with {in_channels} in_channels")
+    else:
+        logging.info(f"making identity attention with {in_channels} in_channels")
+
+    match attn_type:
+        case AttentionType.VANILLA:
+            return AttnBlock(in_channels, norm_type=norm_type)
+        case AttentionType.NONE:
+            return nn.Identity(in_channels)
+        case AttentionType.LINEAR:
+            raise NotImplementedError(f"Attention type {attn_type.value} is not supported yet.")
+        case _:
+            raise ValueError(f"Unknown attention type: {attn_type}")
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        ch,
+        out_ch,
+        ch_mult=(1, 2, 4, 8),
+        num_res_blocks,
+        attn_resolutions,
+        dropout=0.0,
+        resamp_with_conv=True,
+        in_channels,
+        resolution,
+        z_channels,
+        double_z=True,
+        attn_type="vanilla",
+        mid_block_add_attention=True,
+        norm_type="group",
+        causality_axis=CausalityAxis.WIDTH.value,
+        **ignore_kwargs,
+    ):
+        super().__init__()
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.z_channels = z_channels
+        self.double_z = double_z
+        self.norm_type = norm_type
+        # Convert string to enum if needed (for config loading)
+        causality_axis = CausalityAxis.str_to_enum(causality_axis)
+        self.attn_type = AttentionType.str_to_enum(attn_type)
+
+        # downsampling
+        self.conv_in = make_conv2d(
+            in_channels,
+            self.ch,
+            kernel_size=3,
+            stride=1,
+            causality_axis=causality_axis,
+        )
+
+        self.non_linearity = nn.SiLU()
+
+        curr_res = resolution
+        in_ch_mult = (1,) + tuple(ch_mult)
+        self.in_ch_mult = in_ch_mult
+        self.down = nn.ModuleList()
+
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch * in_ch_mult[i_level]
+            block_out = ch * ch_mult[i_level]
+
+            for _ in range(self.num_res_blocks):
+                block.append(
+                    ResnetBlock(
+                        in_channels=block_in,
+                        out_channels=block_out,
+                        temb_channels=self.temb_ch,
+                        dropout=dropout,
+                        norm_type=self.norm_type,
+                        causality_axis=causality_axis,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type))
+
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions - 1:
+                down.downsample = Downsample(block_in, resamp_with_conv, causality_axis=causality_axis)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+            norm_type=self.norm_type,
+            causality_axis=causality_axis,
+        )
+        if mid_block_add_attention:
+            self.mid.attn_1 = make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type)
+        else:
+            self.mid.attn_1 = nn.Identity()
+        self.mid.block_2 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+            norm_type=self.norm_type,
+            causality_axis=causality_axis,
+        )
+
+        # end
+        self.norm_out = Normalize(block_in, normtype=self.norm_type)
+        self.conv_out = make_conv2d(
+            block_in,
+            2 * z_channels if double_z else z_channels,
+            kernel_size=3,
+            stride=1,
+            causality_axis=causality_axis,
+        )
+
+    def forward(self, x):
+        """
+        Forward pass through the encoder.
+
+        Args:
+            x: Input tensor of shape [batch, channels, time, n_mels]
+
+        Returns:
+            Encoded latent representation
+        """
+        feature_maps = [self.conv_in(x)]
+
+        # Process each resolution level (from high to low resolution)
+        for resolution_level in range(self.num_resolutions):
+            # Apply residual blocks at current resolution level
+            for block_idx in range(self.num_res_blocks):
+                # Apply ResNet block with optional timestep embedding
+                current_features = self.down[resolution_level].block[block_idx](feature_maps[-1], temb=None)
+
+                # Apply attention if configured for this resolution level
+                if len(self.down[resolution_level].attn) > 0:
+                    current_features = self.down[resolution_level].attn[block_idx](current_features)
+
+                # Store processed features
+                feature_maps.append(current_features)
+
+            # Downsample spatial dimensions (except at the final resolution level)
+            if resolution_level != self.num_resolutions - 1:
+                downsampled_features = self.down[resolution_level].downsample(feature_maps[-1])
+                feature_maps.append(downsampled_features)
+
+        # === MIDDLE PROCESSING PHASE ===
+        # Take the lowest resolution features for middle processing
+        bottleneck_features = feature_maps[-1]
+
+        # Apply first middle ResNet block
+        bottleneck_features = self.mid.block_1(bottleneck_features, temb=None)
+
+        # Apply middle attention block
+        bottleneck_features = self.mid.attn_1(bottleneck_features)
+
+        # Apply second middle ResNet block
+        bottleneck_features = self.mid.block_2(bottleneck_features, temb=None)
+
+        # === OUTPUT PHASE ===
+        # Normalize the bottleneck features
+        output_features = self.norm_out(bottleneck_features)
+
+        # Apply non-linearity (SiLU activation)
+        output_features = self.non_linearity(output_features)
+
+        # Final convolution to produce latent representation
+        # [batch, channels, time, n_mels] -> [batch, 2 * z_channels if double_z else z_channels, time, n_mels]
+        return self.conv_out(output_features)
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        ch,
+        out_ch,
+        ch_mult=(1, 2, 4, 8),
+        num_res_blocks,
+        attn_resolutions,
+        dropout=0.0,
+        resamp_with_conv=True,
+        in_channels,
+        resolution,
+        z_channels,
+        give_pre_end=False,
+        tanh_out=False,
+        attn_type="vanilla",
+        mid_block_add_attention=True,
+        norm_type="group",
+        causality_axis=CausalityAxis.WIDTH.value,
+        **ignorekwargs,
+    ):
+        super().__init__()
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.out_ch = out_ch
+        self.give_pre_end = give_pre_end
+        self.tanh_out = tanh_out
+        self.norm_type = norm_type
+        self.z_channels = z_channels
+        # Convert string to enum if needed (for config loading)
+        causality_axis = CausalityAxis.str_to_enum(causality_axis)
+        self.attn_type = AttentionType.str_to_enum(attn_type)
+
+        # compute block_in and curr_res at lowest res
+        block_in = ch * ch_mult[self.num_resolutions - 1]
+        curr_res = resolution // 2 ** (self.num_resolutions - 1)
+        self.z_shape = (1, z_channels, curr_res, curr_res)
+
+        # z to block_in
+        self.conv_in = make_conv2d(z_channels, block_in, kernel_size=3, stride=1, causality_axis=causality_axis)
+
+        self.non_linearity = nn.SiLU()
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+            norm_type=self.norm_type,
+            causality_axis=causality_axis,
+        )
+        if mid_block_add_attention:
+            self.mid.attn_1 = make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type)
+        else:
+            self.mid.attn_1 = nn.Identity()
+        self.mid.block_2 = ResnetBlock(
+            in_channels=block_in,
+            out_channels=block_in,
+            temb_channels=self.temb_ch,
+            dropout=dropout,
+            norm_type=self.norm_type,
+            causality_axis=causality_axis,
+        )
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks + 1):
+                block.append(
+                    ResnetBlock(
+                        in_channels=block_in,
+                        out_channels=block_out,
+                        temb_channels=self.temb_ch,
+                        dropout=dropout,
+                        norm_type=self.norm_type,
+                        causality_axis=causality_axis,
+                    )
+                )
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=self.attn_type, norm_type=self.norm_type))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv, causality_axis=causality_axis)
+                curr_res = curr_res * 2
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in, normtype=self.norm_type)
+        self.conv_out = make_conv2d(block_in, out_ch, kernel_size=3, stride=1, causality_axis=causality_axis)
+
+    def _adjust_output_shape(self, decoded_output, target_shape):
+        """
+        Adjust output shape to match target dimensions for variable-length audio.
+
+        This function handles the common case where decoded audio spectrograms need to be
+        resized to match a specific target shape.
+
+        Args:
+            decoded_output: Tensor of shape (batch, channels, time, frequency)
+            target_shape: Target shape tuple (batch, channels, time, frequency)
+
+        Returns:
+            Tensor adjusted to match target_shape exactly
+        """
+        # Current output shape: (batch, channels, time, frequency)
+        _, _, current_time, current_freq = decoded_output.shape
+        _, target_channels, target_time, target_freq = target_shape
+
+        # Step 1: Crop first to avoid exceeding target dimensions
+        decoded_output = decoded_output[
+            :, :target_channels, : min(current_time, target_time), : min(current_freq, target_freq)
+        ]
+
+        # Step 2: Calculate padding needed for time and frequency dimensions
+        time_padding_needed = target_time - decoded_output.shape[2]
+        freq_padding_needed = target_freq - decoded_output.shape[3]
+
+        # Step 3: Apply padding if needed
+        if time_padding_needed > 0 or freq_padding_needed > 0:
+            # PyTorch padding format: (pad_left, pad_right, pad_top, pad_bottom)
+            # For audio: pad_left/right = frequency, pad_top/bottom = time
+            padding = (
+                0,
+                max(freq_padding_needed, 0),  # frequency padding (left, right)
+                0,
+                max(time_padding_needed, 0),  # time padding (top, bottom)
+            )
+            decoded_output = F.pad(decoded_output, padding)
+
+        # Step 4: Final safety crop to ensure exact target shape
+        decoded_output = decoded_output[:, :target_channels, :target_time, :target_freq]
+
+        return decoded_output
+
+    def get_config(self):
+        return {
+            "ch": self.ch,
+            "out_ch": self.out_ch,
+            "ch_mult": self.ch_mult,
+            "num_res_blocks": self.num_res_blocks,
+            "in_channels": self.in_channels,
+            "resolution": self.resolution,
+            "z_channels": self.z_channels,
+        }
+
+    def forward(self, latent_features, target_shape=None):
+        """
+        Decode latent features back to audio spectrograms.
+
+        Args:
+            latent_features: Encoded latent representation of shape (batch, channels, height, width)
+            target_shape: Optional target output shape (batch, channels, time, frequency)
+                         If provided, output will be cropped/padded to match this shape
+
+        Returns:
+            Reconstructed audio spectrogram of shape (batch, channels, time, frequency)
+        """
+        assert target_shape is not None, "Target shape is required for CausalAudioAutoencoder Decoder"
+
+        # Transform latent features to decoder's internal feature dimension
+        hidden_features = self.conv_in(latent_features)
+
+        # Middle processing
+        hidden_features = self.mid.block_1(hidden_features, temb=None)
+        hidden_features = self.mid.attn_1(hidden_features)
+        hidden_features = self.mid.block_2(hidden_features, temb=None)
+
+        # Upsampling
+        # Progressively increase spatial resolution from lowest to highest
+        for resolution_level in reversed(range(self.num_resolutions)):
+            # Apply residual blocks at current resolution level
+            for block_index in range(self.num_res_blocks + 1):
+                hidden_features = self.up[resolution_level].block[block_index](hidden_features, temb=None)
+
+                if len(self.up[resolution_level].attn) > 0:
+                    hidden_features = self.up[resolution_level].attn[block_index](hidden_features)
+
+            if resolution_level != 0:
+                hidden_features = self.up[resolution_level].upsample(hidden_features)
+
+        # Output
+        if self.give_pre_end:
+            # Return intermediate features before final processing (for debugging/analysis)
+            decoded_output = hidden_features
+        else:
+            # Standard output path: normalize, activate, and convert to output channels
+            # Final normalization layer
+            hidden_features = self.norm_out(hidden_features)
+
+            # Apply SiLU (Swish) activation function
+            hidden_features = self.non_linearity(hidden_features)
+
+            # Final convolution to map to output channels (typically 2 for stereo audio)
+            decoded_output = self.conv_out(hidden_features)
+
+            # Optional tanh activation to bound output values to [-1, 1] range
+            if self.tanh_out:
+                decoded_output = torch.tanh(decoded_output)
+
+        # Adjust shape for audio data
+        if target_shape is not None:
+            decoded_output = self._adjust_output_shape(decoded_output, target_shape)
+
+        return decoded_output
+
+
+class processor(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.register_buffer("std-of-means", torch.empty(128))
+        self.register_buffer("mean-of-means", torch.empty(128))
+
+    def un_normalize(self, x):
+        return (x * self.get_buffer("std-of-means").to(x)) + self.get_buffer("mean-of-means").to(x)
+
+    def normalize(self, x):
+        return (x - self.get_buffer("mean-of-means").to(x)) / self.get_buffer("std-of-means").to(x)
+
+
+class CausalAudioAutoencoder(nn.Module):
+    def __init__(self, config=None):
+        super().__init__()
+
+        if config is None:
+            config = self._guess_config()
+
+        # Extract encoder and decoder configs from the new format
+        model_config = config.get("model", {}).get("params", {})
+        variables_config = config.get("variables", {})
+
+        self.sampling_rate = variables_config.get(
+            "sampling_rate",
+            model_config.get("sampling_rate", config.get("sampling_rate", 16000)),
+        )
+        encoder_config = model_config.get("encoder", model_config.get("ddconfig", {}))
+        decoder_config = model_config.get("decoder", encoder_config)
+
+        # Load mel spectrogram parameters
+        self.mel_bins = encoder_config.get("mel_bins", 64)
+        self.mel_hop_length = model_config.get("preprocessing", {}).get("stft", {}).get("hop_length", 160)
+        self.n_fft = model_config.get("preprocessing", {}).get("stft", {}).get("filter_length", 1024)
+
+        # Store causality configuration at VAE level (not just in encoder internals)
+        causality_axis_value = encoder_config.get("causality_axis", CausalityAxis.WIDTH.value)
+        self.causality_axis = CausalityAxis.str_to_enum(causality_axis_value)
+        self.is_causal = self.causality_axis == CausalityAxis.HEIGHT
+
+        self.encoder = Encoder(**encoder_config)
+        self.decoder = Decoder(**decoder_config)
+
+        self.per_channel_statistics = processor()
+
+    def _guess_config(self):
+        encoder_config = {
+            # Required parameters - based on ltx-video-av-1679000 model metadata
+            "ch": 128,
+            "out_ch": 8,
+            "ch_mult": [1, 2, 4],  # Based on metadata: [1, 2, 4] not [1, 2, 4, 8]
+            "num_res_blocks": 2,
+            "attn_resolutions": [],  # Based on metadata: empty list, no attention
+            "dropout": 0.0,
+            "resamp_with_conv": True,
+            "in_channels": 2,  # stereo
+            "resolution": 256,
+            "z_channels": 8,
+            "double_z": True,
+            "attn_type": "vanilla",
+            "mid_block_add_attention": False,  # Based on metadata: false
+            "norm_type": "pixel",
+            "causality_axis": "height",  # Based on metadata
+            "mel_bins": 64,  # Based on metadata: mel_bins = 64
+        }
+
+        decoder_config = {
+            # Inherits encoder config, can override specific params
+            **encoder_config,
+            "out_ch": 2,  # Stereo audio output (2 channels)
+            "give_pre_end": False,
+            "tanh_out": False,
+        }
+
+        config = {
+            "_class_name": "CausalAudioAutoencoder",
+            "sampling_rate": 16000,
+            "model": {
+                "params": {
+                    "encoder": encoder_config,
+                    "decoder": decoder_config,
+                }
+            },
+        }
+
+        return config
+
+    def get_config(self):
+        return {
+            "sampling_rate": self.sampling_rate,
+            "mel_bins": self.mel_bins,
+            "mel_hop_length": self.mel_hop_length,
+            "n_fft": self.n_fft,
+            "causality_axis": self.causality_axis.value,
+            "is_causal": self.is_causal,
+        }
+
+    def encode(self, x):
+        return self.encoder(x)
+
+    def decode(self, x, target_shape=None):
+        return self.decoder(x, target_shape=target_shape)

comfy/ldm/lightricks/vocoders/vocoder.py

@@ -0,0 +1,213 @@
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+import comfy.ops
+import numpy as np
+
+ops = comfy.ops.disable_weight_init
+
+LRELU_SLOPE = 0.1
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList(
+            [
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[0],
+                    padding=get_padding(kernel_size, dilation[0]),
+                ),
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[1],
+                    padding=get_padding(kernel_size, dilation[1]),
+                ),
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[2],
+                    padding=get_padding(kernel_size, dilation[2]),
+                ),
+            ]
+        )
+
+        self.convs2 = nn.ModuleList(
+            [
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=1,
+                    padding=get_padding(kernel_size, 1),
+                ),
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=1,
+                    padding=get_padding(kernel_size, 1),
+                ),
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=1,
+                    padding=get_padding(kernel_size, 1),
+                ),
+            ]
+        )
+
+    def forward(self, x):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            xt = c2(xt)
+            x = xt + x
+        return x
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList(
+            [
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[0],
+                    padding=get_padding(kernel_size, dilation[0]),
+                ),
+                ops.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    1,
+                    dilation=dilation[1],
+                    padding=get_padding(kernel_size, dilation[1]),
+                ),
+            ]
+        )
+
+    def forward(self, x):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c(xt)
+            x = xt + x
+        return x
+
+
+class Vocoder(torch.nn.Module):
+    """
+    Vocoder model for synthesizing audio from spectrograms, based on: https://github.com/jik876/hifi-gan.
+
+    """
+
+    def __init__(self, config=None):
+        super(Vocoder, self).__init__()
+
+        if config is None:
+            config = self.get_default_config()
+
+        resblock_kernel_sizes = config.get("resblock_kernel_sizes", [3, 7, 11])
+        upsample_rates = config.get("upsample_rates", [6, 5, 2, 2, 2])
+        upsample_kernel_sizes = config.get("upsample_kernel_sizes", [16, 15, 8, 4, 4])
+        resblock_dilation_sizes = config.get("resblock_dilation_sizes", [[1, 3, 5], [1, 3, 5], [1, 3, 5]])
+        upsample_initial_channel = config.get("upsample_initial_channel", 1024)
+        stereo = config.get("stereo", True)
+        resblock = config.get("resblock", "1")
+
+        self.output_sample_rate = config.get("output_sample_rate")
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        in_channels = 128 if stereo else 64
+        self.conv_pre = ops.Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3)
+        resblock_class = ResBlock1 if resblock == "1" else ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                ops.ConvTranspose1d(
+                    upsample_initial_channel // (2**i),
+                    upsample_initial_channel // (2 ** (i + 1)),
+                    k,
+                    u,
+                    padding=(k - u) // 2,
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock_class(ch, k, d))
+
+        out_channels = 2 if stereo else 1
+        self.conv_post = ops.Conv1d(ch, out_channels, 7, 1, padding=3)
+
+        self.upsample_factor = np.prod([self.ups[i].stride[0] for i in range(len(self.ups))])
+
+    def get_default_config(self):
+        """Generate default configuration for the vocoder."""
+
+        config = {
+            "resblock_kernel_sizes": [3, 7, 11],
+            "upsample_rates": [6, 5, 2, 2, 2],
+            "upsample_kernel_sizes": [16, 15, 8, 4, 4],
+            "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+            "upsample_initial_channel": 1024,
+            "stereo": True,
+            "resblock": "1",
+        }
+
+        return config
+
+    def forward(self, x):
+        """
+        Forward pass of the vocoder.
+
+        Args:
+            x: Input spectrogram tensor. Can be:
+               - 3D: (batch_size, channels, time_steps) for mono
+               - 4D: (batch_size, 2, channels, time_steps) for stereo
+
+        Returns:
+            Audio tensor of shape (batch_size, out_channels, audio_length)
+        """
+        if x.dim() == 4:  # stereo
+            assert x.shape[1] == 2, "Input must have 2 channels for stereo"
+            x = torch.cat((x[:, 0, :, :], x[:, 1, :, :]), dim=1)
+        x = self.conv_pre(x)
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x

comfy/ldm/lumina/model.py

@@ -491,7 +491,8 @@ class NextDiT(nn.Module):
                 for layer_id in range(n_layers)
             ]
         )
-        self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        # This norm final is in the lumina 2.0 code but isn't actually used for anything.
+        # self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
         self.final_layer = FinalLayer(dim, patch_size, self.out_channels, z_image_modulation=z_image_modulation, operation_settings=operation_settings)
 
         if self.pad_tokens_multiple is not None:
@@ -625,7 +626,7 @@ class NextDiT(nn.Module):
             if pooled is not None:
                 pooled = self.clip_text_pooled_proj(pooled)
             else:
-                pooled = torch.zeros((1, self.clip_text_dim), device=x.device, dtype=x.dtype)
+                pooled = torch.zeros((x.shape[0], self.clip_text_dim), device=x.device, dtype=x.dtype)
 
             adaln_input = self.time_text_embed(torch.cat((t, pooled), dim=-1))
 

comfy/ldm/modules/attention.py

@@ -30,6 +30,13 @@ except ImportError as e:
             raise e
         exit(-1)
 
+SAGE_ATTENTION3_IS_AVAILABLE = False
+try:
+    from sageattn3 import sageattn3_blackwell
+    SAGE_ATTENTION3_IS_AVAILABLE = True
+except ImportError:
+    pass
+
 FLASH_ATTENTION_IS_AVAILABLE = False
 try:
     from flash_attn import flash_attn_func
@@ -563,6 +570,93 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
             out = out.reshape(b, -1, heads * dim_head)
     return out
 
+@wrap_attn
+def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    exception_fallback = False
+    if (q.device.type != "cuda" or
+        q.dtype not in (torch.float16, torch.bfloat16) or
+        mask is not None):
+        return attention_pytorch(
+            q, k, v, heads,
+            mask=mask,
+            attn_precision=attn_precision,
+            skip_reshape=skip_reshape,
+            skip_output_reshape=skip_output_reshape,
+            **kwargs
+        )
+
+    if skip_reshape:
+        B, H, L, D = q.shape
+        if H != heads:
+            return attention_pytorch(
+                q, k, v, heads,
+                mask=mask,
+                attn_precision=attn_precision,
+                skip_reshape=True,
+                skip_output_reshape=skip_output_reshape,
+                **kwargs
+            )
+        q_s, k_s, v_s = q, k, v
+        N = q.shape[2]
+        dim_head = D
+    else:
+        B, N, inner_dim = q.shape
+        if inner_dim % heads != 0:
+            return attention_pytorch(
+                q, k, v, heads,
+                mask=mask,
+                attn_precision=attn_precision,
+                skip_reshape=False,
+                skip_output_reshape=skip_output_reshape,
+                **kwargs
+            )
+        dim_head = inner_dim // heads
+
+    if dim_head >= 256 or N <= 1024:
+        return attention_pytorch(
+                q, k, v, heads,
+                mask=mask,
+                attn_precision=attn_precision,
+                skip_reshape=skip_reshape,
+                skip_output_reshape=skip_output_reshape,
+                **kwargs
+            )
+
+    if not skip_reshape:
+        q_s, k_s, v_s = map(
+            lambda t: t.view(B, -1, heads, dim_head).permute(0, 2, 1, 3).contiguous(),
+            (q, k, v),
+        )
+        B, H, L, D = q_s.shape
+
+    try:
+        out = sageattn3_blackwell(q_s, k_s, v_s, is_causal=False)
+    except Exception as e:
+        exception_fallback = True
+        logging.error("Error running SageAttention3: %s, falling back to pytorch attention.", e)
+
+    if exception_fallback:
+        if not skip_reshape:
+            del q_s, k_s, v_s
+        return attention_pytorch(
+                q, k, v, heads,
+                mask=mask,
+                attn_precision=attn_precision,
+                skip_reshape=False,
+                skip_output_reshape=skip_output_reshape,
+                **kwargs
+            )
+
+    if skip_reshape:
+        if not skip_output_reshape:
+            out = out.permute(0, 2, 1, 3).reshape(B, L, H * D)
+    else:
+        if skip_output_reshape:
+            pass
+        else:
+            out = out.permute(0, 2, 1, 3).reshape(B, L, H * D)
+
+    return out
 
 try:
     @torch.library.custom_op("flash_attention::flash_attn", mutates_args=())
@@ -650,6 +744,8 @@ optimized_attention_masked = optimized_attention
 # register core-supported attention functions
 if SAGE_ATTENTION_IS_AVAILABLE:
     register_attention_function("sage", attention_sage)
+if SAGE_ATTENTION3_IS_AVAILABLE:
+    register_attention_function("sage3", attention3_sage)
 if FLASH_ATTENTION_IS_AVAILABLE:
     register_attention_function("flash", attention_flash)
 if model_management.xformers_enabled():

comfy/ldm/modules/diffusionmodules/model.py

@@ -394,7 +394,8 @@ class Model(nn.Module):
                  attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
                  resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
         super().__init__()
-        if use_linear_attn: attn_type = "linear"
+        if use_linear_attn:
+            attn_type = "linear"
         self.ch = ch
         self.temb_ch = self.ch*4
         self.num_resolutions = len(ch_mult)
@@ -548,7 +549,8 @@ class Encoder(nn.Module):
                  conv3d=False, time_compress=None,
                  **ignore_kwargs):
         super().__init__()
-        if use_linear_attn: attn_type = "linear"
+        if use_linear_attn:
+            attn_type = "linear"
         self.ch = ch
         self.temb_ch = 0
         self.num_resolutions = len(ch_mult)

comfy/ldm/modules/ema.py

@@ -45,7 +45,7 @@ class LitEma(nn.Module):
                     shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
                     shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
                 else:
-                    assert not key in self.m_name2s_name
+                    assert key not in self.m_name2s_name
 
     def copy_to(self, model):
         m_param = dict(model.named_parameters())
@@ -54,7 +54,7 @@ class LitEma(nn.Module):
             if m_param[key].requires_grad:
                 m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
             else:
-                assert not key in self.m_name2s_name
+                assert key not in self.m_name2s_name
 
     def store(self, parameters):
         """

comfy/ldm/util.py

@@ -71,7 +71,7 @@ def count_params(model, verbose=False):
 
 
 def instantiate_from_config(config):
-    if not "target" in config:
+    if "target" not in config:
         if config == '__is_first_stage__':
             return None
         elif config == "__is_unconditional__":

comfy/model_base.py

@@ -20,6 +20,7 @@ import comfy.ldm.hunyuan3dv2_1
 import comfy.ldm.hunyuan3dv2_1.hunyuandit
 import torch
 import logging
+import comfy.ldm.lightricks.av_model
 from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
 from comfy.ldm.cascade.stage_c import StageC
 from comfy.ldm.cascade.stage_b import StageB
@@ -946,7 +947,7 @@ class GenmoMochi(BaseModel):
 
 class LTXV(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.model.LTXVModel) #TODO
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.model.LTXVModel)
 
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
@@ -977,6 +978,60 @@ class LTXV(BaseModel):
     def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
         return latent_image
 
+class LTXAV(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.av_model.LTXAVModel) #TODO
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            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)
+
+        out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25))
+
+        denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+
+        audio_denoise_mask = None
+        if denoise_mask is not None and "latent_shapes" in kwargs:
+            denoise_mask = utils.unpack_latents(denoise_mask, kwargs["latent_shapes"])
+            if len(denoise_mask) > 1:
+                audio_denoise_mask = denoise_mask[1]
+            denoise_mask = denoise_mask[0]
+
+        if denoise_mask is not None:
+            out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask)
+
+        if audio_denoise_mask is not None:
+            out["audio_denoise_mask"] = comfy.conds.CONDRegular(audio_denoise_mask)
+
+        keyframe_idxs = kwargs.get("keyframe_idxs", None)
+        if keyframe_idxs is not None:
+            out['keyframe_idxs'] = comfy.conds.CONDRegular(keyframe_idxs)
+
+        latent_shapes = kwargs.get("latent_shapes", None)
+        if latent_shapes is not None:
+            out['latent_shapes'] = comfy.conds.CONDConstant(latent_shapes)
+
+        return out
+
+    def process_timestep(self, timestep, x, denoise_mask=None, audio_denoise_mask=None, **kwargs):
+        v_timestep = timestep
+        a_timestep = timestep
+
+        if denoise_mask is not None:
+            v_timestep = self.diffusion_model.patchifier.patchify(((denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1)))[:, :1])[0]
+        if audio_denoise_mask is not None:
+            a_timestep = self.diffusion_model.a_patchifier.patchify(((audio_denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (audio_denoise_mask.ndim - 1)))[:, :1, :, :1])[0]
+
+        return v_timestep, a_timestep
+
+    def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
+        return latent_image
+
 class HunyuanVideo(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)
@@ -1110,7 +1165,7 @@ class Lumina2(BaseModel):
             if 'num_tokens' not in out:
                 out['num_tokens'] = comfy.conds.CONDConstant(cross_attn.shape[1])
 
-        clip_text_pooled = kwargs["pooled_output"]  # Newbie
+        clip_text_pooled = kwargs.get("pooled_output", None)  # NewBie
         if clip_text_pooled is not None:
             out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled)
 

comfy/model_detection.py

@@ -305,7 +305,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
     if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv
         dit_config = {}
-        dit_config["image_model"] = "ltxv"
+        dit_config["image_model"] = "ltxav" if f'{key_prefix}audio_adaln_single.linear.weight' in state_dict_keys else "ltxv"
         dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.')
         shape = state_dict['{}transformer_blocks.0.attn2.to_k.weight'.format(key_prefix)].shape
         dit_config["attention_head_dim"] = shape[0] // 32
@@ -430,8 +430,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["rope_theta"] = 10000.0
             dit_config["ffn_dim_multiplier"] = 4.0
             ctd_weight = state_dict.get('{}clip_text_pooled_proj.0.weight'.format(key_prefix), None)
-            if ctd_weight is not None:
+            if ctd_weight is not None:  # NewBie
                 dit_config["clip_text_dim"] = ctd_weight.shape[0]
+                # NewBie also sets axes_lens = [1024, 512, 512] but it's not used in ComfyUI
         elif dit_config["dim"] == 3840:  # Z image
             dit_config["n_heads"] = 30
             dit_config["n_kv_heads"] = 30

comfy/model_management.py

@@ -1019,8 +1019,8 @@ NUM_STREAMS = 0
 if args.async_offload is not None:
     NUM_STREAMS = args.async_offload
 else:
-    #  Enable by default on Nvidia
+    #  Enable by default on Nvidia and AMD
-    if is_nvidia():
+    if is_nvidia() or is_amd():
         NUM_STREAMS = 2
 
 if args.disable_async_offload:
@@ -1126,6 +1126,16 @@ if not args.disable_pinned_memory:
 
 PINNING_ALLOWED_TYPES = set(["Parameter", "QuantizedTensor"])
 
+def discard_cuda_async_error():
+    try:
+        a = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
+        b = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
+        _ = a + b
+        torch.cuda.synchronize()
+    except torch.AcceleratorError:
+        #Dump it! We already know about it from the synchronous return
+        pass
+
 def pin_memory(tensor):
     global TOTAL_PINNED_MEMORY
     if MAX_PINNED_MEMORY <= 0:
@@ -1158,6 +1168,9 @@ def pin_memory(tensor):
         PINNED_MEMORY[ptr] = size
         TOTAL_PINNED_MEMORY += size
         return True
+    else:
+        logging.warning("Pin error.")
+        discard_cuda_async_error()
 
     return False
 
@@ -1186,6 +1199,9 @@ def unpin_memory(tensor):
         if len(PINNED_MEMORY) == 0:
             TOTAL_PINNED_MEMORY = 0
         return True
+    else:
+        logging.warning("Unpin error.")
+        discard_cuda_async_error()
 
     return False
 
@@ -1526,6 +1542,10 @@ def soft_empty_cache(force=False):
 def unload_all_models():
     free_memory(1e30, get_torch_device())
 
+def debug_memory_summary():
+    if is_amd() or is_nvidia():
+        return torch.cuda.memory.memory_summary()
+    return ""
 
 #TODO: might be cleaner to put this somewhere else
 import threading

comfy/sd.py

@@ -55,6 +55,8 @@ import comfy.text_encoders.hunyuan_image
 import comfy.text_encoders.z_image
 import comfy.text_encoders.ovis
 import comfy.text_encoders.kandinsky5
+import comfy.text_encoders.jina_clip_2
+import comfy.text_encoders.newbie
 
 import comfy.model_patcher
 import comfy.lora
@@ -1008,6 +1010,7 @@ class CLIPType(Enum):
     OVIS = 21
     KANDINSKY5 = 22
     KANDINSKY5_IMAGE = 23
+    NEWBIE = 24
 
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
@@ -1038,6 +1041,8 @@ class TEModel(Enum):
     MISTRAL3_24B_PRUNED_FLUX2 = 15
     QWEN3_4B = 16
     QWEN3_2B = 17
+    GEMMA_3_12B = 18
+    JINA_CLIP_2 = 19
 
 
 def detect_te_model(sd):
@@ -1047,6 +1052,8 @@ def detect_te_model(sd):
         return TEModel.CLIP_H
     if "text_model.encoder.layers.0.mlp.fc1.weight" in sd:
         return TEModel.CLIP_L
+    if "model.encoder.layers.0.mixer.Wqkv.weight" in sd:
+        return TEModel.JINA_CLIP_2
     if "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd:
         weight = sd["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
         if weight.shape[-1] == 4096:
@@ -1061,6 +1068,8 @@ def detect_te_model(sd):
             return TEModel.BYT5_SMALL_GLYPH
         return TEModel.T5_BASE
     if 'model.layers.0.post_feedforward_layernorm.weight' in sd:
+        if 'model.layers.47.self_attn.q_norm.weight' in sd:
+            return TEModel.GEMMA_3_12B
         if 'model.layers.0.self_attn.q_norm.weight' in sd:
             return TEModel.GEMMA_3_4B
         return TEModel.GEMMA_2_2B
@@ -1207,6 +1216,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         elif te_model == TEModel.QWEN3_2B:
             clip_target.clip = comfy.text_encoders.ovis.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.ovis.OvisTokenizer
+        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
         else:
             # clip_l
             if clip_type == CLIPType.SD3:
@@ -1262,6 +1274,21 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         elif clip_type == CLIPType.KANDINSKY5_IMAGE:
             clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage
+        elif clip_type == CLIPType.LTXV:
+            clip_target.clip = comfy.text_encoders.lt.ltxav_te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.lt.LTXAVGemmaTokenizer
+            tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
+        elif clip_type == CLIPType.NEWBIE:
+            clip_target.clip = comfy.text_encoders.newbie.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.newbie.NewBieTokenizer
+            if "model.layers.0.self_attn.q_norm.weight" in clip_data[0]:
+                clip_data_gemma = clip_data[0]
+                clip_data_jina = clip_data[1]
+            else:
+                clip_data_gemma = clip_data[1]
+                clip_data_jina = clip_data[0]
+            tokenizer_data["gemma_spiece_model"] = clip_data_gemma.get("spiece_model", None)
+            tokenizer_data["jina_spiece_model"] = clip_data_jina.get("spiece_model", None)
         else:
             clip_target.clip = sdxl_clip.SDXLClipModel
             clip_target.tokenizer = sdxl_clip.SDXLTokenizer

comfy/sd1_clip.py

@@ -466,7 +466,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
     return embed_out
 
 class SDTokenizer:
-    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, pad_left=False, tokenizer_data={}, tokenizer_args={}):
+    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, pad_left=False, disable_weights=False, tokenizer_data={}, tokenizer_args={}):
         if tokenizer_path is None:
             tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
         self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args)
@@ -513,6 +513,8 @@ class SDTokenizer:
         self.embedding_size = embedding_size
         self.embedding_key = embedding_key
 
+        self.disable_weights = disable_weights
+
     def _try_get_embedding(self, embedding_name:str):
         '''
         Takes a potential embedding name and tries to retrieve it.
@@ -547,7 +549,7 @@ class SDTokenizer:
         min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding)
 
         text = escape_important(text)
-        if kwargs.get("disable_weights", False):
+        if kwargs.get("disable_weights", self.disable_weights):
             parsed_weights = [(text, 1.0)]
         else:
             parsed_weights = token_weights(text, 1.0)

comfy/supported_models.py

@@ -836,6 +836,21 @@ class LTXV(supported_models_base.BASE):
         t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.lt.LTXVT5Tokenizer, comfy.text_encoders.lt.ltxv_te(**t5_detect))
 
+class LTXAV(LTXV):
+    unet_config = {
+        "image_model": "ltxav",
+    }
+
+    latent_format = latent_formats.LTXAV
+
+    def __init__(self, unet_config):
+        super().__init__(unet_config)
+        self.memory_usage_factor = 0.055  # TODO
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.LTXAV(self, device=device)
+        return out
+
 class HunyuanVideo(supported_models_base.BASE):
     unet_config = {
         "image_model": "hunyuan_video",
@@ -1536,6 +1551,6 @@ class Kandinsky5Image(Kandinsky5):
         return supported_models_base.ClipTarget(comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage, comfy.text_encoders.kandinsky5.te(**hunyuan_detect))
 
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5]
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5]
 
 models += [SVD_img2vid]

comfy/taesd/taehv.py

@@ -154,7 +154,8 @@ class TAEHV(nn.Module):
             self._show_progress_bar = value
 
     def encode(self, x, **kwargs):
-        if self.patch_size > 1: x = F.pixel_unshuffle(x, self.patch_size)
+        if self.patch_size > 1:
+            x = F.pixel_unshuffle(x, self.patch_size)
         x = x.movedim(2, 1)  # [B, C, T, H, W] -> [B, T, C, H, W]
         if x.shape[1] % 4 != 0:
             # pad at end to multiple of 4
@@ -167,5 +168,6 @@ class TAEHV(nn.Module):
     def decode(self, x, **kwargs):
         x = self.process_in(x).movedim(2, 1)  # [B, C, T, H, W] -> [B, T, C, H, W]
         x = apply_model_with_memblocks(self.decoder, x, self.parallel, self.show_progress_bar)
-        if self.patch_size > 1: x = F.pixel_shuffle(x, self.patch_size)
+        if self.patch_size > 1:
+            x = F.pixel_shuffle(x, self.patch_size)
         return x[:, self.frames_to_trim:].movedim(2, 1)

comfy/text_encoders/jina_clip_2.py

@@ -0,0 +1,219 @@
+# Jina CLIP v2 and Jina Embeddings v3 both use their modified XLM-RoBERTa architecture. Reference implementation:
+# Jina CLIP v2 (both text and vision): https://huggingface.co/jinaai/jina-clip-implementation/blob/39e6a55ae971b59bea6e44675d237c99762e7ee2/modeling_clip.py
+# Jina XLM-RoBERTa (text only): http://huggingface.co/jinaai/xlm-roberta-flash-implementation/blob/2b6bc3f30750b3a9648fe9b63448c09920efe9be/modeling_xlm_roberta.py
+
+from dataclasses import dataclass
+
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+import comfy.model_management
+import comfy.ops
+from comfy import sd1_clip
+from .spiece_tokenizer import SPieceTokenizer
+
+class JinaClip2Tokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer = tokenizer_data.get("spiece_model", None)
+        # The official NewBie uses max_length=8000, but Jina Embeddings v3 actually supports 8192
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=1024, embedding_key='jina_clip_2', tokenizer_class=SPieceTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=False, max_length=8192, min_length=1, pad_token=1, end_token=2, tokenizer_args={"add_bos": True, "add_eos": True}, tokenizer_data=tokenizer_data)
+
+    def state_dict(self):
+        return {"spiece_model": self.tokenizer.serialize_model()}
+
+class JinaClip2TokenizerWrapper(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, tokenizer=JinaClip2Tokenizer, name="jina_clip_2")
+
+# https://huggingface.co/jinaai/jina-embeddings-v3/blob/343dbf534c76fe845f304fa5c2d1fd87e1e78918/config.json
+@dataclass
+class XLMRobertaConfig:
+    vocab_size: int = 250002
+    type_vocab_size: int = 1
+    hidden_size: int = 1024
+    num_hidden_layers: int = 24
+    num_attention_heads: int = 16
+    rotary_emb_base: float = 20000.0
+    intermediate_size: int = 4096
+    hidden_act: str = "gelu"
+    hidden_dropout_prob: float = 0.1
+    attention_probs_dropout_prob: float = 0.1
+    layer_norm_eps: float = 1e-05
+    bos_token_id: int = 0
+    eos_token_id: int = 2
+    pad_token_id: int = 1
+
+class XLMRobertaEmbeddings(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        embed_dim = config.hidden_size
+        self.word_embeddings = ops.Embedding(config.vocab_size, embed_dim, padding_idx=config.pad_token_id, device=device, dtype=dtype)
+        self.token_type_embeddings = ops.Embedding(config.type_vocab_size, embed_dim, device=device, dtype=dtype)
+
+    def forward(self, input_ids=None, embeddings=None):
+        if input_ids is not None and embeddings is None:
+            embeddings = self.word_embeddings(input_ids)
+
+        if embeddings is not None:
+            token_type_ids = torch.zeros(embeddings.shape[1], device=embeddings.device, dtype=torch.int32)
+            token_type_embeddings = self.token_type_embeddings(token_type_ids)
+            embeddings = embeddings + token_type_embeddings
+        return embeddings
+
+class RotaryEmbedding(nn.Module):
+    def __init__(self, dim, base, device=None):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached = None
+        self._sin_cached = None
+
+    def _update_cos_sin_cache(self, seqlen, device=None, dtype=None):
+        if seqlen > self._seq_len_cached or self._cos_cached is None or self._cos_cached.device != device or self._cos_cached.dtype != dtype:
+            self._seq_len_cached = seqlen
+            t = torch.arange(seqlen, device=device, dtype=torch.float32)
+            freqs = torch.outer(t, self.inv_freq.to(device=t.device))
+            emb = torch.cat((freqs, freqs), dim=-1)
+            self._cos_cached = emb.cos().to(dtype)
+            self._sin_cached = emb.sin().to(dtype)
+
+    def forward(self, q, k):
+        batch, seqlen, heads, head_dim = q.shape
+        self._update_cos_sin_cache(seqlen, device=q.device, dtype=q.dtype)
+
+        cos = self._cos_cached[:seqlen].view(1, seqlen, 1, head_dim)
+        sin = self._sin_cached[:seqlen].view(1, seqlen, 1, head_dim)
+
+        def rotate_half(x):
+            size = x.shape[-1] // 2
+            x1, x2 = x[..., :size], x[..., size:]
+            return torch.cat((-x2, x1), dim=-1)
+
+        q_embed = (q * cos) + (rotate_half(q) * sin)
+        k_embed = (k * cos) + (rotate_half(k) * sin)
+        return q_embed, k_embed
+
+class MHA(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = embed_dim // config.num_attention_heads
+
+        self.rotary_emb = RotaryEmbedding(self.head_dim, config.rotary_emb_base, device=device)
+        self.Wqkv = ops.Linear(embed_dim, 3 * embed_dim, device=device, dtype=dtype)
+        self.out_proj = ops.Linear(embed_dim, embed_dim, device=device, dtype=dtype)
+
+    def forward(self, x, mask=None, optimized_attention=None):
+        qkv = self.Wqkv(x)
+        batch_size, seq_len, _ = qkv.shape
+        qkv = qkv.view(batch_size, seq_len, 3, self.num_heads, self.head_dim)
+        q, k, v = qkv.unbind(2)
+
+        q, k = self.rotary_emb(q, k)
+
+        # NHD -> HND
+        q = q.transpose(1, 2)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+
+        out = optimized_attention(q, k, v, heads=self.num_heads, mask=mask, skip_reshape=True)
+        return self.out_proj(out)
+
+class MLP(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.fc1 = ops.Linear(config.hidden_size, config.intermediate_size, device=device, dtype=dtype)
+        self.activation = F.gelu
+        self.fc2 = ops.Linear(config.intermediate_size, config.hidden_size, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.activation(x)
+        x = self.fc2(x)
+        return x
+
+class Block(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.mixer = MHA(config, device=device, dtype=dtype, ops=ops)
+        self.dropout1 = nn.Dropout(config.hidden_dropout_prob)
+        self.norm1 = ops.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, device=device, dtype=dtype)
+        self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
+        self.dropout2 = nn.Dropout(config.hidden_dropout_prob)
+        self.norm2 = ops.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, device=device, dtype=dtype)
+
+    def forward(self, hidden_states, mask=None, optimized_attention=None):
+        mixer_out = self.mixer(hidden_states, mask=mask, optimized_attention=optimized_attention)
+        hidden_states = self.norm1(self.dropout1(mixer_out) + hidden_states)
+        mlp_out = self.mlp(hidden_states)
+        hidden_states = self.norm2(self.dropout2(mlp_out) + hidden_states)
+        return hidden_states
+
+class XLMRobertaEncoder(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.layers = nn.ModuleList([Block(config, device=device, dtype=dtype, ops=ops) for _ in range(config.num_hidden_layers)])
+
+    def forward(self, hidden_states, attention_mask=None):
+        optimized_attention = comfy.ldm.modules.attention.optimized_attention_for_device(hidden_states.device, mask=attention_mask is not None, small_input=True)
+        for layer in self.layers:
+            hidden_states = layer(hidden_states, mask=attention_mask, optimized_attention=optimized_attention)
+        return hidden_states
+
+class XLMRobertaModel_(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.embeddings = XLMRobertaEmbeddings(config, device=device, dtype=dtype, ops=ops)
+        self.emb_ln = ops.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, device=device, dtype=dtype)
+        self.emb_drop = nn.Dropout(config.hidden_dropout_prob)
+        self.encoder = XLMRobertaEncoder(config, device=device, dtype=dtype, ops=ops)
+
+    def forward(self, input_ids, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]):
+        x = self.embeddings(input_ids=input_ids, embeddings=embeds)
+        x = self.emb_ln(x)
+        x = self.emb_drop(x)
+
+        mask = None
+        if attention_mask is not None:
+            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, 1, attention_mask.shape[-1]))
+            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
+
+        sequence_output = self.encoder(x, attention_mask=mask)
+
+        # Mean pool, see https://huggingface.co/jinaai/jina-clip-implementation/blob/39e6a55ae971b59bea6e44675d237c99762e7ee2/hf_model.py
+        pooled_output = None
+        if attention_mask is None:
+            pooled_output = sequence_output.mean(dim=1)
+        else:
+            attention_mask = attention_mask.to(sequence_output.dtype)
+            pooled_output = (sequence_output * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(dim=-1, keepdim=True)
+
+        # Intermediate output is not yet implemented, use None for placeholder
+        return sequence_output, None, pooled_output
+
+class XLMRobertaModel(nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        self.config = XLMRobertaConfig(**config_dict)
+        self.model = XLMRobertaModel_(self.config, device=device, dtype=dtype, ops=operations)
+        self.num_layers = self.config.num_hidden_layers
+
+    def get_input_embeddings(self):
+        return self.model.embeddings.word_embeddings
+
+    def set_input_embeddings(self, embeddings):
+        self.model.embeddings.word_embeddings = embeddings
+
+    def forward(self, *args, **kwargs):
+        return self.model(*args, **kwargs)
+
+class JinaClip2TextModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, textmodel_json_config={}, model_class=XLMRobertaModel, special_tokens={"start": 0, "end": 2, "pad": 1}, enable_attention_masks=True, return_attention_masks=True, model_options=model_options)
+
+class JinaClip2TextModelWrapper(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, clip_model=JinaClip2TextModel, name="jina_clip_2", model_options=model_options)

comfy/text_encoders/llama.py

@@ -3,13 +3,12 @@ import torch.nn as nn
 from dataclasses import dataclass
 from typing import Optional, Any
 import math
-import logging
 
 from comfy.ldm.modules.attention import optimized_attention_for_device
 import comfy.model_management
 import comfy.ldm.common_dit
+import comfy.clip_model
 
-import comfy.model_management
 from . import qwen_vl
 
 @dataclass
@@ -177,7 +176,7 @@ class Gemma3_4B_Config:
     num_key_value_heads: int = 4
     max_position_embeddings: int = 131072
     rms_norm_eps: float = 1e-6
-    rope_theta = [10000.0, 1000000.0]
+    rope_theta = [1000000.0, 10000.0]
     transformer_type: str = "gemma3"
     head_dim = 256
     rms_norm_add = True
@@ -186,10 +185,35 @@ class Gemma3_4B_Config:
     rope_dims = None
     q_norm = "gemma3"
     k_norm = "gemma3"
-    sliding_attention = [False, False, False, False, False, 1024]
+    sliding_attention = [1024, 1024, 1024, 1024, 1024, False]
-    rope_scale = [1.0, 8.0]
+    rope_scale = [8.0, 1.0]
     final_norm: bool = True
 
+@dataclass
+class Gemma3_12B_Config:
+    vocab_size: int = 262208
+    hidden_size: int = 3840
+    intermediate_size: int = 15360
+    num_hidden_layers: int = 48
+    num_attention_heads: int = 16
+    num_key_value_heads: int = 8
+    max_position_embeddings: int = 131072
+    rms_norm_eps: float = 1e-6
+    rope_theta = [1000000.0, 10000.0]
+    transformer_type: str = "gemma3"
+    head_dim = 256
+    rms_norm_add = True
+    mlp_activation = "gelu_pytorch_tanh"
+    qkv_bias = False
+    rope_dims = None
+    q_norm = "gemma3"
+    k_norm = "gemma3"
+    sliding_attention = [1024, 1024, 1024, 1024, 1024, False]
+    rope_scale = [8.0, 1.0]
+    final_norm: bool = True
+    vision_config = {"num_channels": 3, "hidden_act": "gelu_pytorch_tanh", "hidden_size": 1152, "image_size": 896, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 14}
+    mm_tokens_per_image = 256
+
 class RMSNorm(nn.Module):
     def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None):
         super().__init__()
@@ -370,7 +394,7 @@ class TransformerBlockGemma2(nn.Module):
         self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
         self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
 
-        if config.sliding_attention is not None:  # TODO: implement. (Not that necessary since models are trained on less than 1024 tokens)
+        if config.sliding_attention is not None:
             self.sliding_attention = config.sliding_attention[index % len(config.sliding_attention)]
         else:
             self.sliding_attention = False
@@ -387,7 +411,12 @@ class TransformerBlockGemma2(nn.Module):
         if self.transformer_type == 'gemma3':
             if self.sliding_attention:
                 if x.shape[1] > self.sliding_attention:
-                    logging.warning("Warning: sliding attention not implemented, results may be incorrect")
+                    sliding_mask = torch.full((x.shape[1], x.shape[1]), float("-inf"), device=x.device, dtype=x.dtype)
+                    sliding_mask.tril_(diagonal=-self.sliding_attention)
+                    if attention_mask is not None:
+                        attention_mask = attention_mask + sliding_mask
+                    else:
+                        attention_mask = sliding_mask
                 freqs_cis = freqs_cis[1]
             else:
                 freqs_cis = freqs_cis[0]
@@ -517,6 +546,41 @@ class Llama2_(nn.Module):
 
         return x, intermediate
 
+
+class Gemma3MultiModalProjector(torch.nn.Module):
+    def __init__(self, config, dtype, device, operations):
+        super().__init__()
+
+        self.mm_input_projection_weight = nn.Parameter(
+            torch.empty(config.vision_config["hidden_size"], config.hidden_size, device=device, dtype=dtype)
+        )
+
+        self.mm_soft_emb_norm = RMSNorm(config.vision_config["hidden_size"], eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+
+        self.patches_per_image = int(config.vision_config["image_size"] // config.vision_config["patch_size"])
+        self.tokens_per_side = int(config.mm_tokens_per_image**0.5)
+        self.kernel_size = self.patches_per_image // self.tokens_per_side
+        self.avg_pool = nn.AvgPool2d(kernel_size=self.kernel_size, stride=self.kernel_size)
+
+    def forward(self, vision_outputs: torch.Tensor):
+        batch_size, _, seq_length = vision_outputs.shape
+
+        reshaped_vision_outputs = vision_outputs.transpose(1, 2)
+        reshaped_vision_outputs = reshaped_vision_outputs.reshape(
+            batch_size, seq_length, self.patches_per_image, self.patches_per_image
+        )
+        reshaped_vision_outputs = reshaped_vision_outputs.contiguous()
+
+        pooled_vision_outputs = self.avg_pool(reshaped_vision_outputs)
+        pooled_vision_outputs = pooled_vision_outputs.flatten(2)
+        pooled_vision_outputs = pooled_vision_outputs.transpose(1, 2)
+
+        normed_vision_outputs = self.mm_soft_emb_norm(pooled_vision_outputs)
+
+        projected_vision_outputs = torch.matmul(normed_vision_outputs, comfy.model_management.cast_to_device(self.mm_input_projection_weight, device=normed_vision_outputs.device, dtype=normed_vision_outputs.dtype))
+        return projected_vision_outputs.type_as(vision_outputs)
+
+
 class BaseLlama:
     def get_input_embeddings(self):
         return self.model.embed_tokens
@@ -633,3 +697,21 @@ class Gemma3_4B(BaseLlama, torch.nn.Module):
 
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
+
+class Gemma3_12B(BaseLlama, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Gemma3_12B_Config(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.multi_modal_projector = Gemma3MultiModalProjector(config, dtype, device, operations)
+        self.vision_model = comfy.clip_model.CLIPVision(config.vision_config, dtype, device, operations)
+        self.dtype = dtype
+        self.image_size = config.vision_config["image_size"]
+
+    def preprocess_embed(self, embed, device):
+        if embed["type"] == "image":
+            image = comfy.clip_model.clip_preprocess(embed["data"], size=self.image_size, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], crop=True)
+            return self.multi_modal_projector(self.vision_model(image.to(device, dtype=torch.float32))[0]), None
+        return None, None

comfy/text_encoders/lt.py

@@ -1,7 +1,11 @@
 from comfy import sd1_clip
 import os
 from transformers import T5TokenizerFast
+from .spiece_tokenizer import SPieceTokenizer
 import comfy.text_encoders.genmo
+from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
+import torch
+import comfy.utils
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -16,3 +20,110 @@ class LTXVT5Tokenizer(sd1_clip.SD1Tokenizer):
 
 def ltxv_te(*args, **kwargs):
     return comfy.text_encoders.genmo.mochi_te(*args, **kwargs)
+
+
+class Gemma3_12BTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer = tokenizer_data.get("spiece_model", None)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=3840, embedding_key='gemma3_12b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
+
+    def state_dict(self):
+        return {"spiece_model": self.tokenizer.serialize_model()}
+
+class LTXAVGemmaTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma3_12b", tokenizer=Gemma3_12BTokenizer)
+
+class Gemma3_12BModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="all", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
+        llama_scaled_fp8 = model_options.get("gemma_scaled_fp8", None)
+        if llama_scaled_fp8 is not None:
+            model_options = model_options.copy()
+            model_options["scaled_fp8"] = llama_scaled_fp8
+
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_12B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template="{}", image_embeds=None, **kwargs):
+        text = llama_template.format(text)
+        text_tokens = super().tokenize_with_weights(text, return_word_ids)
+        embed_count = 0
+        for k in text_tokens:
+            tt = text_tokens[k]
+            for r in tt:
+                for i in range(len(r)):
+                    if r[i][0] == 262144:
+                        if image_embeds is not None and embed_count < image_embeds.shape[0]:
+                            r[i] = ({"type": "embedding", "data": image_embeds[embed_count], "original_type": "image"},) + r[i][1:]
+                            embed_count += 1
+        return text_tokens
+
+class LTXAVTEModel(torch.nn.Module):
+    def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_options={}):
+        super().__init__()
+        self.dtypes = set()
+        self.dtypes.add(dtype)
+
+        self.gemma3_12b = Gemma3_12BModel(device=device, dtype=dtype_llama, model_options=model_options, layer="all", layer_idx=None)
+        self.dtypes.add(dtype_llama)
+
+        operations = self.gemma3_12b.operations # TODO
+        self.text_embedding_projection = operations.Linear(3840 * 49, 3840, bias=False, dtype=dtype, device=device)
+
+        self.audio_embeddings_connector = Embeddings1DConnector(
+            split_rope=True,
+            double_precision_rope=True,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        self.video_embeddings_connector = Embeddings1DConnector(
+            split_rope=True,
+            double_precision_rope=True,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+    def set_clip_options(self, options):
+        self.gemma3_12b.set_clip_options(options)
+
+    def reset_clip_options(self):
+        self.gemma3_12b.reset_clip_options()
+
+    def encode_token_weights(self, token_weight_pairs):
+        token_weight_pairs = token_weight_pairs["gemma3_12b"]
+
+        out, pooled, extra = self.gemma3_12b.encode_token_weights(token_weight_pairs)
+        out_device = out.device
+        out = out.movedim(1, -1).to(self.text_embedding_projection.weight.device)
+        out = 8.0 * (out - out.mean(dim=(1, 2), keepdim=True)) / (out.amax(dim=(1, 2), keepdim=True) - out.amin(dim=(1, 2), keepdim=True) + 1e-6)
+        out = out.reshape((out.shape[0], out.shape[1], -1))
+        out = self.text_embedding_projection(out)
+        out_vid = self.video_embeddings_connector(out)[0]
+        out_audio = self.audio_embeddings_connector(out)[0]
+        out = torch.concat((out_vid, out_audio), dim=-1)
+
+        return out.to(out_device), pooled
+
+    def load_sd(self, sd):
+        if "model.layers.47.self_attn.q_norm.weight" in sd:
+            return self.gemma3_12b.load_sd(sd)
+        else:
+            sdo = comfy.utils.state_dict_prefix_replace(sd, {"text_embedding_projection.aggregate_embed.weight": "text_embedding_projection.weight", "model.diffusion_model.video_embeddings_connector.": "video_embeddings_connector.", "model.diffusion_model.audio_embeddings_connector.": "audio_embeddings_connector."}, filter_keys=True)
+            if len(sdo) == 0:
+                sdo = sd
+
+            return self.load_state_dict(sdo, strict=False)
+
+
+def ltxav_te(dtype_llama=None, llama_scaled_fp8=None):
+    class LTXAVTEModel_(LTXAVTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options:
+                model_options = model_options.copy()
+                model_options["llama_scaled_fp8"] = llama_scaled_fp8
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)
+    return LTXAVTEModel_

comfy/text_encoders/lumina2.py

@@ -14,7 +14,7 @@ class Gemma2BTokenizer(sd1_clip.SDTokenizer):
 class Gemma3_4BTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer = tokenizer_data.get("spiece_model", None)
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, disable_weights=True, tokenizer_data=tokenizer_data)
 
     def state_dict(self):
         return {"spiece_model": self.tokenizer.serialize_model()}
@@ -33,6 +33,11 @@ class Gemma2_2BModel(sd1_clip.SDClipModel):
 
 class Gemma3_4BModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
+        llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
+        if llama_quantization_metadata is not None:
+            model_options = model_options.copy()
+            model_options["quantization_metadata"] = llama_quantization_metadata
+
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
 class LuminaModel(sd1_clip.SD1ClipModel):

comfy/text_encoders/newbie.py

@@ -0,0 +1,62 @@
+import torch
+
+import comfy.model_management
+import comfy.text_encoders.jina_clip_2
+import comfy.text_encoders.lumina2
+
+class NewBieTokenizer:
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        self.gemma = comfy.text_encoders.lumina2.Gemma3_4BTokenizer(embedding_directory=embedding_directory, tokenizer_data={"spiece_model": tokenizer_data["gemma_spiece_model"]})
+        self.jina = comfy.text_encoders.jina_clip_2.JinaClip2Tokenizer(embedding_directory=embedding_directory, tokenizer_data={"spiece_model": tokenizer_data["jina_spiece_model"]})
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
+        out = {}
+        out["gemma"] = self.gemma.tokenize_with_weights(text, return_word_ids, **kwargs)
+        out["jina"] = self.jina.tokenize_with_weights(text, return_word_ids, **kwargs)
+        return out
+
+    def untokenize(self, token_weight_pair):
+        raise NotImplementedError
+
+    def state_dict(self):
+        return {}
+
+class NewBieTEModel(torch.nn.Module):
+    def __init__(self, dtype_gemma=None, device="cpu", dtype=None, model_options={}):
+        super().__init__()
+        dtype_gemma = comfy.model_management.pick_weight_dtype(dtype_gemma, dtype, device)
+        self.gemma = comfy.text_encoders.lumina2.Gemma3_4BModel(device=device, dtype=dtype_gemma, model_options=model_options)
+        self.jina = comfy.text_encoders.jina_clip_2.JinaClip2TextModel(device=device, dtype=dtype, model_options=model_options)
+        self.dtypes = {dtype, dtype_gemma}
+
+    def set_clip_options(self, options):
+        self.gemma.set_clip_options(options)
+        self.jina.set_clip_options(options)
+
+    def reset_clip_options(self):
+        self.gemma.reset_clip_options()
+        self.jina.reset_clip_options()
+
+    def encode_token_weights(self, token_weight_pairs):
+        token_weight_pairs_gemma = token_weight_pairs["gemma"]
+        token_weight_pairs_jina = token_weight_pairs["jina"]
+
+        gemma_out, gemma_pooled, gemma_extra = self.gemma.encode_token_weights(token_weight_pairs_gemma)
+        jina_out, jina_pooled, jina_extra = self.jina.encode_token_weights(token_weight_pairs_jina)
+
+        return gemma_out, jina_pooled, gemma_extra
+
+    def load_sd(self, sd):
+        if "model.layers.0.self_attn.q_norm.weight" in sd:
+            return self.gemma.load_sd(sd)
+        else:
+            return self.jina.load_sd(sd)
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+    class NewBieTEModel_(NewBieTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["llama_quantization_metadata"] = llama_quantization_metadata
+            super().__init__(dtype_gemma=dtype_llama, device=device, dtype=dtype, model_options=model_options)
+    return NewBieTEModel_

comfy/utils.py

@@ -1198,7 +1198,7 @@ def unpack_latents(combined_latent, latent_shapes):
             combined_latent = combined_latent[:, :, cut:]
             output_tensors.append(tens.reshape([tens.shape[0]] + list(shape)[1:]))
     else:
-        output_tensors = combined_latent
+        output_tensors = [combined_latent]
     return output_tensors
 
 def detect_layer_quantization(state_dict, prefix):
@@ -1230,6 +1230,8 @@ def convert_old_quants(state_dict, model_prefix="", metadata={}):
             out_sd = {}
             layers = {}
             for k in list(state_dict.keys()):
+                if k == scaled_fp8_key:
+                    continue
                 if not k.startswith(model_prefix):
                     out_sd[k] = state_dict[k]
                     continue

comfy_api/latest/__init__.py

@@ -10,7 +10,6 @@ from ._input_impl import VideoFromFile, VideoFromComponents
 from ._util import VideoCodec, VideoContainer, VideoComponents, MESH, VOXEL
 from . import _io_public as io
 from . import _ui_public as ui
-# from comfy_api.latest._resources import _RESOURCES as resources  #noqa: F401
 from comfy_execution.utils import get_executing_context
 from comfy_execution.progress import get_progress_state, PreviewImageTuple
 from PIL import Image

comfy_api/latest/_io.py

@@ -26,11 +26,9 @@ if TYPE_CHECKING:
     from comfy_api.input import VideoInput
 from comfy_api.internal import (_ComfyNodeInternal, _NodeOutputInternal, classproperty, copy_class, first_real_override, is_class,
     prune_dict, shallow_clone_class)
-from ._resources import Resources, ResourcesLocal
 from comfy_execution.graph_utils import ExecutionBlocker
-from ._util import MESH, VOXEL
+from ._util import MESH, VOXEL, SVG as _SVG
 
-# from comfy_extras.nodes_images import SVG as SVG_ # NOTE: needs to be moved before can be imported due to circular reference
 
 class FolderType(str, Enum):
     input = "input"
@@ -77,16 +75,6 @@ class NumberDisplay(str, Enum):
     slider = "slider"
 
 
-class _StringIOType(str):
-    def __ne__(self, value: object) -> bool:
-        if self == "*" or value == "*":
-            return False
-        if not isinstance(value, str):
-            return True
-        a = frozenset(self.split(","))
-        b = frozenset(value.split(","))
-        return not (b.issubset(a) or a.issubset(b))
-
 class _ComfyType(ABC):
     Type = Any
     io_type: str = None
@@ -126,8 +114,7 @@ def comfytype(io_type: str, **kwargs):
             new_cls.__module__ = cls.__module__
             new_cls.__doc__ = cls.__doc__
             # assign ComfyType attributes, if needed
-        # NOTE: use __ne__ trick for io_type (see node_typing.IO.__ne__ for details)
+        new_cls.io_type = io_type
-        new_cls.io_type = _StringIOType(io_type)
         if hasattr(new_cls, "Input") and new_cls.Input is not None:
             new_cls.Input.Parent = new_cls
         if hasattr(new_cls, "Output") and new_cls.Output is not None:
@@ -166,7 +153,7 @@ class Input(_IO_V3):
     '''
     Base class for a V3 Input.
     '''
-    def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
+    def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None):
         super().__init__()
         self.id = id
         self.display_name = display_name
@@ -174,6 +161,7 @@ class Input(_IO_V3):
         self.tooltip = tooltip
         self.lazy = lazy
         self.extra_dict = extra_dict if extra_dict is not None else {}
+        self.rawLink = raw_link
 
     def as_dict(self):
         return prune_dict({
@@ -181,10 +169,11 @@ class Input(_IO_V3):
             "optional": self.optional,
             "tooltip": self.tooltip,
             "lazy": self.lazy,
+            "rawLink": self.rawLink,
         }) | prune_dict(self.extra_dict)
 
     def get_io_type(self):
-        return _StringIOType(self.io_type)
+        return self.io_type
 
     def get_all(self) -> list[Input]:
         return [self]
@@ -195,8 +184,8 @@ class WidgetInput(Input):
     '''
     def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                  default: Any=None,
-                 socketless: bool=None, widget_type: str=None, force_input: bool=None, extra_dict=None):
+                 socketless: bool=None, widget_type: str=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
-        super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+        super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link)
         self.default = default
         self.socketless = socketless
         self.widget_type = widget_type
@@ -218,13 +207,14 @@ class Output(_IO_V3):
     def __init__(self, id: str=None, display_name: str=None, tooltip: str=None,
                  is_output_list=False):
         self.id = id
-        self.display_name = display_name
+        self.display_name = display_name if display_name else id
         self.tooltip = tooltip
         self.is_output_list = is_output_list
 
     def as_dict(self):
+        display_name = self.display_name if self.display_name else self.id
         return prune_dict({
-            "display_name": self.display_name,
+            "display_name": display_name,
             "tooltip": self.tooltip,
             "is_output_list": self.is_output_list,
         })
@@ -252,8 +242,8 @@ class Boolean(ComfyTypeIO):
         '''Boolean input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: bool=None, label_on: str=None, label_off: str=None,
-                    socketless: bool=None, force_input: bool=None):
+                    socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input)
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link)
             self.label_on = label_on
             self.label_off = label_off
             self.default: bool
@@ -272,8 +262,8 @@ class Int(ComfyTypeIO):
         '''Integer input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: int=None, min: int=None, max: int=None, step: int=None, control_after_generate: bool=None,
-                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None):
+                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input)
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link)
             self.min = min
             self.max = max
             self.step = step
@@ -298,8 +288,8 @@ class Float(ComfyTypeIO):
         '''Float input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: float=None, min: float=None, max: float=None, step: float=None, round: float=None,
-                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None):
+                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input)
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link)
             self.min = min
             self.max = max
             self.step = step
@@ -324,8 +314,8 @@ class String(ComfyTypeIO):
         '''String input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     multiline=False, placeholder: str=None, default: str=None, dynamic_prompts: bool=None,
-                    socketless: bool=None, force_input: bool=None):
+                    socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input)
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link)
             self.multiline = multiline
             self.placeholder = placeholder
             self.dynamic_prompts = dynamic_prompts
@@ -358,12 +348,14 @@ class Combo(ComfyTypeIO):
             image_folder: FolderType=None,
             remote: RemoteOptions=None,
             socketless: bool=None,
+            extra_dict=None,
+            raw_link: bool=None,
         ):
             if isinstance(options, type) and issubclass(options, Enum):
                 options = [v.value for v in options]
             if isinstance(default, Enum):
                 default = default.value
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless)
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, None, extra_dict, raw_link)
             self.multiselect = False
             self.options = options
             self.control_after_generate = control_after_generate
@@ -387,10 +379,6 @@ class Combo(ComfyTypeIO):
             super().__init__(id, display_name, tooltip, is_output_list)
             self.options = options if options is not None else []
 
-        @property
-        def io_type(self):
-            return self.options
-
 @comfytype(io_type="COMBO")
 class MultiCombo(ComfyTypeI):
     '''Multiselect Combo input (dropdown for selecting potentially more than one value).'''
@@ -399,8 +387,8 @@ class MultiCombo(ComfyTypeI):
     class Input(Combo.Input):
         def __init__(self, id: str, options: list[str], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: list[str]=None, placeholder: str=None, chip: bool=None, control_after_generate: bool=None,
-                    socketless: bool=None):
+                    socketless: bool=None, extra_dict=None, raw_link: bool=None):
-            super().__init__(id, options, display_name, optional, tooltip, lazy, default, control_after_generate, socketless=socketless)
+            super().__init__(id, options, display_name, optional, tooltip, lazy, default, control_after_generate, socketless=socketless, extra_dict=extra_dict, raw_link=raw_link)
             self.multiselect = True
             self.placeholder = placeholder
             self.chip = chip
@@ -433,9 +421,9 @@ class Webcam(ComfyTypeIO):
         Type = str
         def __init__(
                 self, id: str, display_name: str=None, optional=False,
-                tooltip: str=None, lazy: bool=None, default: str=None, socketless: bool=None
+                tooltip: str=None, lazy: bool=None, default: str=None, socketless: bool=None, extra_dict=None, raw_link: bool=None
         ):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless)
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, None, extra_dict, raw_link)
 
 
 @comfytype(io_type="MASK")
@@ -656,7 +644,7 @@ class Video(ComfyTypeIO):
 
 @comfytype(io_type="SVG")
 class SVG(ComfyTypeIO):
-    Type = Any # TODO: SVG class is defined in comfy_extras/nodes_images.py, causing circular reference; should be moved to somewhere else before referenced directly in v3
+    Type = _SVG
 
 @comfytype(io_type="LORA_MODEL")
 class LoraModel(ComfyTypeIO):
@@ -788,7 +776,7 @@ class MultiType:
         '''
         Input that permits more than one input type; if `id` is an instance of `ComfyType.Input`, then that input will be used to create a widget (if applicable) with overridden values.
         '''
-        def __init__(self, id: str | Input, types: list[type[_ComfyType] | _ComfyType], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
+        def __init__(self, id: str | Input, types: list[type[_ComfyType] | _ComfyType], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None):
             # if id is an Input, then use that Input with overridden values
             self.input_override = None
             if isinstance(id, Input):
@@ -801,7 +789,7 @@ class MultiType:
                 # if is a widget input, make sure widget_type is set appropriately
                 if isinstance(self.input_override, WidgetInput):
                     self.input_override.widget_type = self.input_override.get_io_type()
-            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link)
             self._io_types = types
 
         @property
@@ -855,8 +843,8 @@ class MatchType(ComfyTypeIO):
 
     class Input(Input):
         def __init__(self, id: str, template: MatchType.Template,
-                    display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
+                    display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link)
             self.template = template
 
         def as_dict(self):
@@ -867,6 +855,8 @@ class MatchType(ComfyTypeIO):
     class Output(Output):
         def __init__(self, template: MatchType.Template, id: str=None, display_name: str=None, tooltip: str=None,
                      is_output_list=False):
+            if not id and not display_name:
+                display_name = "MATCHTYPE"
             super().__init__(id, display_name, tooltip, is_output_list)
             self.template = template
 
@@ -879,24 +869,30 @@ class DynamicInput(Input, ABC):
     '''
     Abstract class for dynamic input registration.
     '''
-    def get_dynamic(self) -> list[Input]:
+    pass
-        return []
-
-    def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
-        pass
 
 
 class DynamicOutput(Output, ABC):
     '''
     Abstract class for dynamic output registration.
     '''
-    def __init__(self, id: str=None, display_name: str=None, tooltip: str=None,
+    pass
-                 is_output_list=False):
-        super().__init__(id, display_name, tooltip, is_output_list)
 
-    def get_dynamic(self) -> list[Output]:
-        return []
 
+def handle_prefix(prefix_list: list[str] | None, id: str | None = None) -> list[str]:
+    if prefix_list is None:
+        prefix_list = []
+    if id is not None:
+        prefix_list = prefix_list + [id]
+    return prefix_list
+
+def finalize_prefix(prefix_list: list[str] | None, id: str | None = None) -> str:
+    assert not (prefix_list is None and id is None)
+    if prefix_list is None:
+        return id
+    elif id is not None:
+        prefix_list = prefix_list + [id]
+    return ".".join(prefix_list)
 
 @comfytype(io_type="COMFY_AUTOGROW_V3")
 class Autogrow(ComfyTypeI):
@@ -933,14 +929,6 @@ class Autogrow(ComfyTypeI):
         def validate(self):
             self.input.validate()
 
-        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
-            real_inputs = []
-            for name, input in self.cached_inputs.items():
-                if name in live_inputs:
-                    real_inputs.append(input)
-            add_to_input_dict_v1(d, real_inputs, live_inputs, curr_prefix)
-            add_dynamic_id_mapping(d, real_inputs, curr_prefix)
-
     class TemplatePrefix(_AutogrowTemplate):
         def __init__(self, input: Input, prefix: str, min: int=1, max: int=10):
             super().__init__(input)
@@ -985,22 +973,45 @@ class Autogrow(ComfyTypeI):
                 "template": self.template.as_dict(),
             })
 
-        def get_dynamic(self) -> list[Input]:
-            return self.template.get_all()
-
         def get_all(self) -> list[Input]:
             return [self] + self.template.get_all()
 
         def validate(self):
             self.template.validate()
 
-        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+    @staticmethod
-            curr_prefix = f"{curr_prefix}{self.id}."
+    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None):
-            # need to remove self from expected inputs dictionary; replaced by template inputs in frontend
+        # NOTE: purposely do not include self in out_dict; instead use only the template inputs
-            for inner_dict in d.values():
+        # need to figure out names based on template type
-                if self.id in inner_dict:
+        is_names = ("names" in value[1]["template"])
-                    del inner_dict[self.id]
+        is_prefix = ("prefix" in value[1]["template"])
-            self.template.expand_schema_for_dynamic(d, live_inputs, curr_prefix)
+        input = value[1]["template"]["input"]
+        if is_names:
+            min = value[1]["template"]["min"]
+            names = value[1]["template"]["names"]
+            max = len(names)
+        elif is_prefix:
+            prefix = value[1]["template"]["prefix"]
+            min = value[1]["template"]["min"]
+            max = value[1]["template"]["max"]
+            names = [f"{prefix}{i}" for i in range(max)]
+        # need to create a new input based on the contents of input
+        template_input = None
+        for _, dict_input in input.items():
+            # for now, get just the first value from dict_input
+            template_input = list(dict_input.values())[0]
+        new_dict = {}
+        for i, name in enumerate(names):
+            expected_id = finalize_prefix(curr_prefix, name)
+            if expected_id in live_inputs:
+                # required
+                if i < min:
+                    type_dict = new_dict.setdefault("required", {})
+                # optional
+                else:
+                    type_dict = new_dict.setdefault("optional", {})
+                type_dict[name] = template_input
+        parse_class_inputs(out_dict, live_inputs, new_dict, curr_prefix)
 
 @comfytype(io_type="COMFY_DYNAMICCOMBO_V3")
 class DynamicCombo(ComfyTypeI):
@@ -1023,23 +1034,6 @@ class DynamicCombo(ComfyTypeI):
             super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
             self.options = options
 
-        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
-            # check if dynamic input's id is in live_inputs
-            if self.id in live_inputs:
-                curr_prefix = f"{curr_prefix}{self.id}."
-                key = live_inputs[self.id]
-                selected_option = None
-                for option in self.options:
-                    if option.key == key:
-                        selected_option = option
-                        break
-                if selected_option is not None:
-                    add_to_input_dict_v1(d, selected_option.inputs, live_inputs, curr_prefix)
-                    add_dynamic_id_mapping(d, selected_option.inputs, curr_prefix, self)
-
-        def get_dynamic(self) -> list[Input]:
-            return [input for option in self.options for input in option.inputs]
-
         def get_all(self) -> list[Input]:
             return [self] + [input for option in self.options for input in option.inputs]
 
@@ -1054,6 +1048,24 @@ class DynamicCombo(ComfyTypeI):
                 for input in option.inputs:
                     input.validate()
 
+    @staticmethod
+    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None):
+        finalized_id = finalize_prefix(curr_prefix)
+        if finalized_id in live_inputs:
+            key = live_inputs[finalized_id]
+            selected_option = None
+            # get options from dict
+            options: list[dict[str, str | dict[str, Any]]] = value[1]["options"]
+            for option in options:
+                if option["key"] == key:
+                    selected_option = option
+                    break
+            if selected_option is not None:
+                parse_class_inputs(out_dict, live_inputs, selected_option["inputs"], curr_prefix)
+                # add self to inputs
+                out_dict[input_type][finalized_id] = value
+                out_dict["dynamic_paths"][finalized_id] = finalize_prefix(curr_prefix, curr_prefix[-1])
+
 @comfytype(io_type="COMFY_DYNAMICSLOT_V3")
 class DynamicSlot(ComfyTypeI):
     Type = dict[str, Any]
@@ -1076,17 +1088,8 @@ class DynamicSlot(ComfyTypeI):
                 self.force_input = True
                 self.slot.force_input = True
 
-        def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
-            if self.id in live_inputs:
-                curr_prefix = f"{curr_prefix}{self.id}."
-                add_to_input_dict_v1(d, self.inputs, live_inputs, curr_prefix)
-                add_dynamic_id_mapping(d, [self.slot] + self.inputs, curr_prefix)
-
-        def get_dynamic(self) -> list[Input]:
-            return [self.slot] + self.inputs
-
         def get_all(self) -> list[Input]:
-            return [self] + [self.slot] + self.inputs
+            return [self.slot] + self.inputs
 
         def as_dict(self):
             return super().as_dict() | prune_dict({
@@ -1100,17 +1103,41 @@ class DynamicSlot(ComfyTypeI):
             for input in self.inputs:
                 input.validate()
 
-def add_dynamic_id_mapping(d: dict[str, Any], inputs: list[Input], curr_prefix: str, self: DynamicInput=None):
+    @staticmethod
-    dynamic = d.setdefault("dynamic_paths", {})
+    def _expand_schema_for_dynamic(out_dict: dict[str, Any], live_inputs: dict[str, Any], value: tuple[str, dict[str, Any]], input_type: str, curr_prefix: list[str] | None):
-    if self is not None:
+        finalized_id = finalize_prefix(curr_prefix)
-        dynamic[self.id] = f"{curr_prefix}{self.id}"
+        if finalized_id in live_inputs:
-    for i in inputs:
+            inputs = value[1]["inputs"]
-        if not isinstance(i, DynamicInput):
+            parse_class_inputs(out_dict, live_inputs, inputs, curr_prefix)
-            dynamic[f"{i.id}"] = f"{curr_prefix}{i.id}"
+            # add self to inputs
+            out_dict[input_type][finalized_id] = value
+            out_dict["dynamic_paths"][finalized_id] = finalize_prefix(curr_prefix, curr_prefix[-1])
+
+DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {}
+def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]):
+    DYNAMIC_INPUT_LOOKUP[io_type] = func
+
+def get_dynamic_input_func(io_type: str) -> Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]:
+    return DYNAMIC_INPUT_LOOKUP[io_type]
+
+def setup_dynamic_input_funcs():
+    # Autogrow.Input
+    register_dynamic_input_func(Autogrow.io_type, Autogrow._expand_schema_for_dynamic)
+    # DynamicCombo.Input
+    register_dynamic_input_func(DynamicCombo.io_type, DynamicCombo._expand_schema_for_dynamic)
+    # DynamicSlot.Input
+    register_dynamic_input_func(DynamicSlot.io_type, DynamicSlot._expand_schema_for_dynamic)
+
+if len(DYNAMIC_INPUT_LOOKUP) == 0:
+    setup_dynamic_input_funcs()
 
 class V3Data(TypedDict):
     hidden_inputs: dict[str, Any]
+    'Dictionary where the keys are the hidden input ids and the values are the values of the hidden inputs.'
     dynamic_paths: dict[str, Any]
+    'Dictionary where the keys are the input ids and the values dictate how to turn the inputs into a nested dictionary.'
+    create_dynamic_tuple: bool
+    'When True, the value of the dynamic input will be in the format (value, path_key).'
 
 class HiddenHolder:
     def __init__(self, unique_id: str, prompt: Any,
@@ -1146,6 +1173,10 @@ class HiddenHolder:
             api_key_comfy_org=d.get(Hidden.api_key_comfy_org, None),
         )
 
+    @classmethod
+    def from_v3_data(cls, v3_data: V3Data | None) -> HiddenHolder:
+        return cls.from_dict(v3_data["hidden_inputs"] if v3_data else None)
+
 class Hidden(str, Enum):
     '''
     Enumerator for requesting hidden variables in nodes.
@@ -1251,61 +1282,56 @@ class Schema:
         - verify ids on inputs and outputs are unique - both internally and in relation to each other
         '''
         nested_inputs: list[Input] = []
-        if self.inputs is not None:
+        for input in self.inputs:
-            for input in self.inputs:
+            if not isinstance(input, DynamicInput):
                 nested_inputs.extend(input.get_all())
-        input_ids = [i.id for i in nested_inputs] if nested_inputs is not None else []
+        input_ids = [i.id for i in nested_inputs]
-        output_ids = [o.id for o in self.outputs] if self.outputs is not None else []
+        output_ids = [o.id for o in self.outputs]
         input_set = set(input_ids)
         output_set = set(output_ids)
-        issues = []
+        issues: list[str] = []
         # verify ids are unique per list
         if len(input_set) != len(input_ids):
             issues.append(f"Input ids must be unique, but {[item for item, count in Counter(input_ids).items() if count > 1]} are not.")
         if len(output_set) != len(output_ids):
             issues.append(f"Output ids must be unique, but {[item for item, count in Counter(output_ids).items() if count > 1]} are not.")
-        # verify ids are unique between lists
-        intersection = input_set & output_set
-        if len(intersection) > 0:
-            issues.append(f"Ids must be unique between inputs and outputs, but {intersection} are not.")
         if len(issues) > 0:
             raise ValueError("\n".join(issues))
         # validate inputs and outputs
-        if self.inputs is not None:
+        for input in self.inputs:
-            for input in self.inputs:
+            input.validate()
-                input.validate()
+        for output in self.outputs:
-        if self.outputs is not None:
+            output.validate()
-            for output in self.outputs:
-                output.validate()
 
     def finalize(self):
         """Add hidden based on selected schema options, and give outputs without ids default ids."""
+        # ensure inputs, outputs, and hidden are lists
+        if self.inputs is None:
+            self.inputs = []
+        if self.outputs is None:
+            self.outputs = []
+        if self.hidden is None:
+            self.hidden = []
         # if is an api_node, will need key-related hidden
         if self.is_api_node:
-            if self.hidden is None:
-                self.hidden = []
             if Hidden.auth_token_comfy_org not in self.hidden:
                 self.hidden.append(Hidden.auth_token_comfy_org)
             if Hidden.api_key_comfy_org not in self.hidden:
                 self.hidden.append(Hidden.api_key_comfy_org)
         # if is an output_node, will need prompt and extra_pnginfo
         if self.is_output_node:
-            if self.hidden is None:
-                self.hidden = []
             if Hidden.prompt not in self.hidden:
                 self.hidden.append(Hidden.prompt)
             if Hidden.extra_pnginfo not in self.hidden:
                 self.hidden.append(Hidden.extra_pnginfo)
         # give outputs without ids default ids
-        if self.outputs is not None:
+        for i, output in enumerate(self.outputs):
-            for i, output in enumerate(self.outputs):
+            if output.id is None:
-                if output.id is None:
+                output.id = f"_{i}_{output.io_type}_"
-                    output.id = f"_{i}_{output.io_type}_"
 
-    def get_v1_info(self, cls, live_inputs: dict[str, Any]=None) -> NodeInfoV1:
+    def get_v1_info(self, cls) -> NodeInfoV1:
-        # NOTE: live_inputs will not be used anymore very soon and this will be done another way
         # get V1 inputs
-        input = create_input_dict_v1(self.inputs, live_inputs)
+        input = create_input_dict_v1(self.inputs)
         if self.hidden:
             for hidden in self.hidden:
                 input.setdefault("hidden", {})[hidden.name] = (hidden.value,)
@@ -1385,33 +1411,54 @@ class Schema:
         )
         return info
 
+def get_finalized_class_inputs(d: dict[str, Any], live_inputs: dict[str, Any], include_hidden=False) -> tuple[dict[str, Any], V3Data]:
+    out_dict = {
+        "required": {},
+        "optional": {},
+        "dynamic_paths": {},
+    }
+    d = d.copy()
+    # ignore hidden for parsing
+    hidden = d.pop("hidden", None)
+    parse_class_inputs(out_dict, live_inputs, d)
+    if hidden is not None and include_hidden:
+        out_dict["hidden"] = hidden
+    v3_data = {}
+    dynamic_paths = out_dict.pop("dynamic_paths", None)
+    if dynamic_paths is not None:
+        v3_data["dynamic_paths"] = dynamic_paths
+    return out_dict, hidden, v3_data
 
-def create_input_dict_v1(inputs: list[Input], live_inputs: dict[str, Any]=None) -> dict:
+def parse_class_inputs(out_dict: dict[str, Any], live_inputs: dict[str, Any], curr_dict: dict[str, Any], curr_prefix: list[str] | None=None) -> None:
+    for input_type, inner_d in curr_dict.items():
+        for id, value in inner_d.items():
+            io_type = value[0]
+            if io_type in DYNAMIC_INPUT_LOOKUP:
+                # dynamic inputs need to be handled with lookup functions
+                dynamic_input_func = get_dynamic_input_func(io_type)
+                new_prefix = handle_prefix(curr_prefix, id)
+                dynamic_input_func(out_dict, live_inputs, value, input_type, new_prefix)
+            else:
+                # non-dynamic inputs get directly transferred
+                finalized_id = finalize_prefix(curr_prefix, id)
+                out_dict[input_type][finalized_id] = value
+                if curr_prefix:
+                    out_dict["dynamic_paths"][finalized_id] = finalized_id
+
+def create_input_dict_v1(inputs: list[Input]) -> dict:
     input = {
         "required": {}
     }
-    add_to_input_dict_v1(input, inputs, live_inputs)
+    for i in inputs:
+        add_to_dict_v1(i, input)
     return input
 
-def add_to_input_dict_v1(d: dict[str, Any], inputs: list[Input], live_inputs: dict[str, Any]=None, curr_prefix=''):
+def add_to_dict_v1(i: Input, d: dict):
-    for i in inputs:
-        if isinstance(i, DynamicInput):
-            add_to_dict_v1(i, d)
-            if live_inputs is not None:
-                i.expand_schema_for_dynamic(d, live_inputs, curr_prefix)
-        else:
-            add_to_dict_v1(i, d)
-
-def add_to_dict_v1(i: Input, d: dict, dynamic_dict: dict=None):
     key = "optional" if i.optional else "required"
     as_dict = i.as_dict()
     # for v1, we don't want to include the optional key
     as_dict.pop("optional", None)
-    if dynamic_dict is None:
+    d.setdefault(key, {})[i.id] = (i.get_io_type(), as_dict)
-        value = (i.get_io_type(), as_dict)
-    else:
-        value = (i.get_io_type(), as_dict, dynamic_dict)
-    d.setdefault(key, {})[i.id] = value
 
 def add_to_dict_v3(io: Input | Output, d: dict):
     d[io.id] = (io.get_io_type(), io.as_dict())
@@ -1423,6 +1470,8 @@ def build_nested_inputs(values: dict[str, Any], v3_data: V3Data):
     values = values.copy()
     result = {}
 
+    create_tuple = v3_data.get("create_dynamic_tuple", False)
+
     for key, path in paths.items():
         parts = path.split(".")
         current = result
@@ -1431,7 +1480,10 @@ def build_nested_inputs(values: dict[str, Any], v3_data: V3Data):
             is_last = (i == len(parts) - 1)
 
             if is_last:
-                current[p] = values.pop(key, None)
+                value = values.pop(key, None)
+                if create_tuple:
+                    value = (value, key)
+                current[p] = value
             else:
                 current = current.setdefault(p, {})
 
@@ -1446,7 +1498,6 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
     SCHEMA = None
 
     # filled in during execution
-    resources: Resources = None
     hidden: HiddenHolder = None
 
     @classmethod
@@ -1493,7 +1544,6 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
         return [name for name in kwargs if kwargs[name] is None]
 
     def __init__(self):
-        self.local_resources: ResourcesLocal = None
         self.__class__.VALIDATE_CLASS()
 
     @classmethod
@@ -1561,7 +1611,7 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
         c_type: type[ComfyNode] = cls if is_class(cls) else type(cls)
         type_clone: type[ComfyNode] = shallow_clone_class(c_type)
         # set hidden
-        type_clone.hidden = HiddenHolder.from_dict(v3_data["hidden_inputs"] if v3_data else None)
+        type_clone.hidden = HiddenHolder.from_v3_data(v3_data)
         return type_clone
 
     @final
@@ -1678,19 +1728,10 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
 
     @final
     @classmethod
-    def INPUT_TYPES(cls, include_hidden=True, return_schema=False, live_inputs=None) -> dict[str, dict] | tuple[dict[str, dict], Schema, V3Data]:
+    def INPUT_TYPES(cls) -> dict[str, dict]:
         schema = cls.FINALIZE_SCHEMA()
-        info = schema.get_v1_info(cls, live_inputs)
+        info = schema.get_v1_info(cls)
-        input = info.input
+        return info.input
-        if not include_hidden:
-            input.pop("hidden", None)
-        if return_schema:
-            v3_data: V3Data = {}
-            dynamic = input.pop("dynamic_paths", None)
-            if dynamic is not None:
-                v3_data["dynamic_paths"] = dynamic
-            return input, schema, v3_data
-        return input
 
     @final
     @classmethod
@@ -1809,7 +1850,7 @@ class NodeOutput(_NodeOutputInternal):
         return self.args if len(self.args) > 0 else None
 
     @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> "NodeOutput":
+    def from_dict(cls, data: dict[str, Any]) -> NodeOutput:
         args = ()
         ui = None
         expand = None
@@ -1904,8 +1945,8 @@ __all__ = [
     "Tracks",
     # Dynamic Types
     "MatchType",
-    # "DynamicCombo",
+    "DynamicCombo",
-    # "Autogrow",
+    "Autogrow",
     # Other classes
     "HiddenHolder",
     "Hidden",

comfy_api/latest/_resources.py

@@ -1,72 +0,0 @@
-from __future__ import annotations
-import comfy.utils
-import folder_paths
-import logging
-from abc import ABC, abstractmethod
-from typing import Any
-import torch
-
-class ResourceKey(ABC):
-    Type = Any
-    def __init__(self):
-        ...
-
-class TorchDictFolderFilename(ResourceKey):
-    '''Key for requesting a torch file via file_name from a folder category.'''
-    Type = dict[str, torch.Tensor]
-    def __init__(self, folder_name: str, file_name: str):
-        self.folder_name = folder_name
-        self.file_name = file_name
-
-    def __hash__(self):
-        return hash((self.folder_name, self.file_name))
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, TorchDictFolderFilename):
-            return False
-        return self.folder_name == other.folder_name and self.file_name == other.file_name
-
-    def __str__(self):
-        return f"{self.folder_name} -> {self.file_name}"
-
-class Resources(ABC):
-    def __init__(self):
-        ...
-
-    @abstractmethod
-    def get(self, key: ResourceKey, default: Any=...) -> Any:
-        pass
-
-class ResourcesLocal(Resources):
-    def __init__(self):
-        super().__init__()
-        self.local_resources: dict[ResourceKey, Any] = {}
-
-    def get(self, key: ResourceKey, default: Any=...) -> Any:
-        cached = self.local_resources.get(key, None)
-        if cached is not None:
-            logging.info(f"Using cached resource '{key}'")
-            return cached
-        logging.info(f"Loading resource '{key}'")
-        to_return = None
-        if isinstance(key, TorchDictFolderFilename):
-            if default is ...:
-                to_return = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise(key.folder_name, key.file_name), safe_load=True)
-            else:
-                full_path = folder_paths.get_full_path(key.folder_name, key.file_name)
-                if full_path is not None:
-                    to_return = comfy.utils.load_torch_file(full_path, safe_load=True)
-
-        if to_return is not None:
-            self.local_resources[key] = to_return
-            return to_return
-        if default is not ...:
-            return default
-        raise Exception(f"Unsupported resource key type: {type(key)}")
-
-
-class _RESOURCES:
-    ResourceKey = ResourceKey
-    TorchDictFolderFilename = TorchDictFolderFilename
-    Resources = Resources
-    ResourcesLocal = ResourcesLocal

comfy_api/latest/_util/__init__.py

@@ -1,5 +1,6 @@
 from .video_types import VideoContainer, VideoCodec, VideoComponents
 from .geometry_types import VOXEL, MESH
+from .image_types import SVG
 
 __all__ = [
     # Utility Types
@@ -8,4 +9,5 @@ __all__ = [
     "VideoComponents",
     "VOXEL",
     "MESH",
+    "SVG",
 ]

comfy_api/latest/_util/image_types.py

@@ -0,0 +1,18 @@
+from io import BytesIO
+
+
+class SVG:
+    """Stores SVG representations via a list of BytesIO objects."""
+
+    def __init__(self, data: list[BytesIO]):
+        self.data = data
+
+    def combine(self, other: 'SVG') -> 'SVG':
+        return SVG(self.data + other.data)
+
+    @staticmethod
+    def combine_all(svgs: list['SVG']) -> 'SVG':
+        all_svgs_list: list[BytesIO] = []
+        for svg_item in svgs:
+            all_svgs_list.extend(svg_item.data)
+        return SVG(all_svgs_list)

comfy_api_nodes/apis/bytedance_api.py

@@ -10,7 +10,7 @@ class Text2ImageTaskCreationRequest(BaseModel):
     size: str | None = Field(None)
     seed: int | None = Field(0, ge=0, le=2147483647)
     guidance_scale: float | None = Field(..., ge=1.0, le=10.0)
-    watermark: bool | None = Field(True)
+    watermark: bool | None = Field(False)
 
 
 class Image2ImageTaskCreationRequest(BaseModel):
@@ -21,7 +21,7 @@ class Image2ImageTaskCreationRequest(BaseModel):
     size: str | None = Field("adaptive")
     seed: int | None = Field(..., ge=0, le=2147483647)
     guidance_scale: float | None = Field(..., ge=1.0, le=10.0)
-    watermark: bool | None = Field(True)
+    watermark: bool | None = Field(False)
 
 
 class Seedream4Options(BaseModel):
@@ -37,7 +37,7 @@ class Seedream4TaskCreationRequest(BaseModel):
     seed: int = Field(..., ge=0, le=2147483647)
     sequential_image_generation: str = Field("disabled")
     sequential_image_generation_options: Seedream4Options = Field(Seedream4Options(max_images=15))
-    watermark: bool = Field(True)
+    watermark: bool = Field(False)
 
 
 class ImageTaskCreationResponse(BaseModel):

comfy_api_nodes/apis/gemini_api.py

@@ -133,6 +133,7 @@ class GeminiImageGenerateContentRequest(BaseModel):
     systemInstruction: GeminiSystemInstructionContent | None = Field(None)
     tools: list[GeminiTool] | None = Field(None)
     videoMetadata: GeminiVideoMetadata | None = Field(None)
+    uploadImagesToStorage: bool = Field(True)
 
 
 class GeminiGenerateContentRequest(BaseModel):

comfy_api_nodes/apis/kling_api.py

@@ -102,3 +102,12 @@ class ImageToVideoWithAudioRequest(BaseModel):
     prompt: str = Field(...)
     mode: str = Field("pro")
     sound: str = Field(..., description="'on' or 'off'")
+
+
+class MotionControlRequest(BaseModel):
+    prompt: str = Field(...)
+    image_url: str = Field(...)
+    video_url: str = Field(...)
+    keep_original_sound: str = Field(...)
+    character_orientation: str = Field(...)
+    mode: str = Field(..., description="'pro' or 'std'")

comfy_api_nodes/nodes_bytedance.py

@@ -112,7 +112,7 @@ class ByteDanceImageNode(IO.ComfyNode):
                 ),
                 IO.Boolean.Input(
                     "watermark",
-                    default=True,
+                    default=False,
                     tooltip='Whether to add an "AI generated" watermark to the image',
                     optional=True,
                 ),
@@ -215,7 +215,7 @@ class ByteDanceImageEditNode(IO.ComfyNode):
                 ),
                 IO.Boolean.Input(
                     "watermark",
-                    default=True,
+                    default=False,
                     tooltip='Whether to add an "AI generated" watermark to the image',
                     optional=True,
                 ),
@@ -229,6 +229,7 @@ class ByteDanceImageEditNode(IO.ComfyNode):
                 IO.Hidden.unique_id,
             ],
             is_api_node=True,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -269,7 +270,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="ByteDanceSeedreamNode",
-            display_name="ByteDance Seedream 4",
+            display_name="ByteDance Seedream 4.5",
             category="api node/image/ByteDance",
             description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.",
             inputs=[
@@ -346,7 +347,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
                 ),
                 IO.Boolean.Input(
                     "watermark",
-                    default=True,
+                    default=False,
                     tooltip='Whether to add an "AI generated" watermark to the image.',
                     optional=True,
                 ),
@@ -380,7 +381,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
         sequential_image_generation: str = "disabled",
         max_images: int = 1,
         seed: int = 0,
-        watermark: bool = True,
+        watermark: bool = False,
         fail_on_partial: bool = True,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
@@ -507,7 +508,7 @@ class ByteDanceTextToVideoNode(IO.ComfyNode):
                 ),
                 IO.Boolean.Input(
                     "watermark",
-                    default=True,
+                    default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
                 ),
@@ -617,7 +618,7 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
                 ),
                 IO.Boolean.Input(
                     "watermark",
-                    default=True,
+                    default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
                 ),
@@ -739,7 +740,7 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
                 ),
                 IO.Boolean.Input(
                     "watermark",
-                    default=True,
+                    default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
                 ),
@@ -862,7 +863,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
                 ),
                 IO.Boolean.Input(
                     "watermark",
-                    default=True,
+                    default=False,
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
                 ),

comfy_api_nodes/nodes_gemini.py

@@ -34,6 +34,7 @@ from comfy_api_nodes.util import (
     ApiEndpoint,
     audio_to_base64_string,
     bytesio_to_image_tensor,
+    download_url_to_image_tensor,
     get_number_of_images,
     sync_op,
     tensor_to_base64_string,
@@ -141,9 +142,11 @@ def get_parts_by_type(response: GeminiGenerateContentResponse, part_type: Litera
         )
     parts = []
     for part in response.candidates[0].content.parts:
-        if part_type == "text" and hasattr(part, "text") and part.text:
+        if part_type == "text" and part.text:
             parts.append(part)
-        elif hasattr(part, "inlineData") and part.inlineData and part.inlineData.mimeType == part_type:
+        elif part.inlineData and part.inlineData.mimeType == part_type:
+            parts.append(part)
+        elif part.fileData and part.fileData.mimeType == part_type:
             parts.append(part)
         # Skip parts that don't match the requested type
     return parts
@@ -163,12 +166,15 @@ def get_text_from_response(response: GeminiGenerateContentResponse) -> str:
     return "\n".join([part.text for part in parts])
 
 
-def get_image_from_response(response: GeminiGenerateContentResponse) -> Input.Image:
+async def get_image_from_response(response: GeminiGenerateContentResponse) -> Input.Image:
     image_tensors: list[Input.Image] = []
     parts = get_parts_by_type(response, "image/png")
     for part in parts:
-        image_data = base64.b64decode(part.inlineData.data)
+        if part.inlineData:
-        returned_image = bytesio_to_image_tensor(BytesIO(image_data))
+            image_data = base64.b64decode(part.inlineData.data)
+            returned_image = bytesio_to_image_tensor(BytesIO(image_data))
+        else:
+            returned_image = await download_url_to_image_tensor(part.fileData.fileUri)
         image_tensors.append(returned_image)
     if len(image_tensors) == 0:
         return torch.zeros((1, 1024, 1024, 4))
@@ -596,7 +602,7 @@ class GeminiImage(IO.ComfyNode):
 
         response = await sync_op(
             cls,
-            endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model}", method="POST"),
+            ApiEndpoint(path=f"/proxy/vertexai/gemini/{model}", method="POST"),
             data=GeminiImageGenerateContentRequest(
                 contents=[
                     GeminiContent(role=GeminiRole.user, parts=parts),
@@ -610,7 +616,7 @@ class GeminiImage(IO.ComfyNode):
             response_model=GeminiGenerateContentResponse,
             price_extractor=calculate_tokens_price,
         )
-        return IO.NodeOutput(get_image_from_response(response), get_text_from_response(response))
+        return IO.NodeOutput(await get_image_from_response(response), get_text_from_response(response))
 
 
 class GeminiImage2(IO.ComfyNode):
@@ -729,7 +735,7 @@ class GeminiImage2(IO.ComfyNode):
 
         response = await sync_op(
             cls,
-            ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model}", method="POST"),
+            ApiEndpoint(path=f"/proxy/vertexai/gemini/{model}", method="POST"),
             data=GeminiImageGenerateContentRequest(
                 contents=[
                     GeminiContent(role=GeminiRole.user, parts=parts),
@@ -743,7 +749,7 @@ class GeminiImage2(IO.ComfyNode):
             response_model=GeminiGenerateContentResponse,
             price_extractor=calculate_tokens_price,
         )
-        return IO.NodeOutput(get_image_from_response(response), get_text_from_response(response))
+        return IO.NodeOutput(await get_image_from_response(response), get_text_from_response(response))
 
 
 class GeminiExtension(ComfyExtension):

comfy_api_nodes/nodes_kling.py

@@ -51,6 +51,7 @@ from comfy_api_nodes.apis import (
 )
 from comfy_api_nodes.apis.kling_api import (
     ImageToVideoWithAudioRequest,
+    MotionControlRequest,
     OmniImageParamImage,
     OmniParamImage,
     OmniParamVideo,
@@ -806,6 +807,7 @@ class OmniProTextToVideoNode(IO.ComfyNode):
                 ),
                 IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
                 IO.Combo.Input("duration", options=[5, 10]),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -825,6 +827,7 @@ class OmniProTextToVideoNode(IO.ComfyNode):
         prompt: str,
         aspect_ratio: str,
         duration: int,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         validate_string(prompt, min_length=1, max_length=2500)
         response = await sync_op(
@@ -836,6 +839,7 @@ class OmniProTextToVideoNode(IO.ComfyNode):
                 prompt=prompt,
                 aspect_ratio=aspect_ratio,
                 duration=str(duration),
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)
@@ -858,7 +862,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
                     tooltip="A text prompt describing the video content. "
                     "This can include both positive and negative descriptions.",
                 ),
-                IO.Combo.Input("duration", options=["5", "10"]),
+                IO.Int.Input("duration", default=5, min=3, max=10, display_mode=IO.NumberDisplay.slider),
                 IO.Image.Input("first_frame"),
                 IO.Image.Input(
                     "end_frame",
@@ -871,6 +875,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
                     optional=True,
                     tooltip="Up to 6 additional reference images.",
                 ),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -892,11 +897,16 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
         first_frame: Input.Image,
         end_frame: Input.Image | None = None,
         reference_images: Input.Image | None = None,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         prompt = normalize_omni_prompt_references(prompt)
         validate_string(prompt, min_length=1, max_length=2500)
         if end_frame is not None and reference_images is not None:
             raise ValueError("The 'end_frame' input cannot be used simultaneously with 'reference_images'.")
+        if duration not in (5, 10) and end_frame is None and reference_images is None:
+            raise ValueError(
+                "Duration is only supported for 5 or 10 seconds if there is no end frame or reference images."
+            )
         validate_image_dimensions(first_frame, min_width=300, min_height=300)
         validate_image_aspect_ratio(first_frame, (1, 2.5), (2.5, 1))
         image_list: list[OmniParamImage] = [
@@ -931,6 +941,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode):
                 prompt=prompt,
                 duration=str(duration),
                 image_list=image_list,
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)
@@ -959,6 +970,7 @@ class OmniProImageToVideoNode(IO.ComfyNode):
                     "reference_images",
                     tooltip="Up to 7 reference images.",
                 ),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -979,6 +991,7 @@ class OmniProImageToVideoNode(IO.ComfyNode):
         aspect_ratio: str,
         duration: int,
         reference_images: Input.Image,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         prompt = normalize_omni_prompt_references(prompt)
         validate_string(prompt, min_length=1, max_length=2500)
@@ -1000,6 +1013,7 @@ class OmniProImageToVideoNode(IO.ComfyNode):
                 aspect_ratio=aspect_ratio,
                 duration=str(duration),
                 image_list=image_list,
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)
@@ -1031,6 +1045,7 @@ class OmniProVideoToVideoNode(IO.ComfyNode):
                     tooltip="Up to 4 additional reference images.",
                     optional=True,
                 ),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -1053,6 +1068,7 @@ class OmniProVideoToVideoNode(IO.ComfyNode):
         reference_video: Input.Video,
         keep_original_sound: bool,
         reference_images: Input.Image | None = None,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         prompt = normalize_omni_prompt_references(prompt)
         validate_string(prompt, min_length=1, max_length=2500)
@@ -1085,6 +1101,7 @@ class OmniProVideoToVideoNode(IO.ComfyNode):
                 duration=str(duration),
                 image_list=image_list if image_list else None,
                 video_list=video_list,
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)
@@ -1114,6 +1131,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
                     tooltip="Up to 4 additional reference images.",
                     optional=True,
                 ),
+                IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -1134,6 +1152,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
         video: Input.Video,
         keep_original_sound: bool,
         reference_images: Input.Image | None = None,
+        resolution: str = "1080p",
     ) -> IO.NodeOutput:
         prompt = normalize_omni_prompt_references(prompt)
         validate_string(prompt, min_length=1, max_length=2500)
@@ -1166,6 +1185,7 @@ class OmniProEditVideoNode(IO.ComfyNode):
                 duration=None,
                 image_list=image_list if image_list else None,
                 video_list=video_list,
+                mode="pro" if resolution == "1080p" else "std",
             ),
         )
         return await finish_omni_video_task(cls, response)
@@ -2159,6 +2179,91 @@ class ImageToVideoWithAudio(IO.ComfyNode):
         return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
 
 
+class MotionControl(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="KlingMotionControl",
+            display_name="Kling Motion Control",
+            category="api node/video/Kling",
+            inputs=[
+                IO.String.Input("prompt", multiline=True),
+                IO.Image.Input("reference_image"),
+                IO.Video.Input(
+                    "reference_video",
+                    tooltip="Motion reference video used to drive movement/expression.\n"
+                    "Duration limits depend on character_orientation:\n"
+                    " - image: 3–10s (max 10s)\n"
+                    " - video: 3–30s (max 30s)",
+                ),
+                IO.Boolean.Input("keep_original_sound", default=True),
+                IO.Combo.Input(
+                    "character_orientation",
+                    options=["video", "image"],
+                    tooltip="Controls where the character's facing/orientation comes from.\n"
+                    "video: movements, expressions, camera moves, and orientation "
+                    "follow the motion reference video (other details via prompt).\n"
+                    "image: movements and expressions still follow the motion reference video, "
+                    "but the character orientation matches the reference image (camera/other details via prompt).",
+                ),
+                IO.Combo.Input("mode", options=["pro", "std"]),
+            ],
+            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,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        reference_image: Input.Image,
+        reference_video: Input.Video,
+        keep_original_sound: bool,
+        character_orientation: str,
+        mode: str,
+    ) -> IO.NodeOutput:
+        validate_string(prompt, max_length=2500)
+        validate_image_dimensions(reference_image, min_width=340, min_height=340)
+        validate_image_aspect_ratio(reference_image, (1, 2.5), (2.5, 1))
+        if character_orientation == "image":
+            validate_video_duration(reference_video, min_duration=3, max_duration=10)
+        else:
+            validate_video_duration(reference_video, min_duration=3, max_duration=30)
+        validate_video_dimensions(reference_video, min_width=340, min_height=340, max_width=3850, max_height=3850)
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/kling/v1/videos/motion-control", method="POST"),
+            response_model=TaskStatusResponse,
+            data=MotionControlRequest(
+                prompt=prompt,
+                image_url=(await upload_images_to_comfyapi(cls, reference_image))[0],
+                video_url=await upload_video_to_comfyapi(cls, reference_video),
+                keep_original_sound="yes" if keep_original_sound else "no",
+                character_orientation=character_orientation,
+                mode=mode,
+            ),
+        )
+        if response.code:
+            raise RuntimeError(
+                f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+            )
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/kling/v1/videos/motion-control/{response.data.task_id}"),
+            response_model=TaskStatusResponse,
+            status_extractor=lambda r: (r.data.task_status if r.data else None),
+        )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
 class KlingExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -2184,6 +2289,7 @@ class KlingExtension(ComfyExtension):
             OmniProImageNode,
             TextToVideoWithAudio,
             ImageToVideoWithAudio,
+            MotionControl,
         ]
 
 

comfy_api_nodes/nodes_topaz.py

@@ -23,10 +23,6 @@ UPSCALER_MODELS_MAP = {
     "Starlight (Astra) Fast": "slf-1",
     "Starlight (Astra) Creative": "slc-1",
 }
-UPSCALER_VALUES_MAP = {
-    "FullHD (1080p)": 1920,
-    "4K (2160p)": 3840,
-}
 
 
 class TopazImageEnhance(IO.ComfyNode):
@@ -214,7 +210,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                 IO.Video.Input("video"),
                 IO.Boolean.Input("upscaler_enabled", default=True),
                 IO.Combo.Input("upscaler_model", options=list(UPSCALER_MODELS_MAP.keys())),
-                IO.Combo.Input("upscaler_resolution", options=list(UPSCALER_VALUES_MAP.keys())),
+                IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"]),
                 IO.Combo.Input(
                     "upscaler_creativity",
                     options=["low", "middle", "high"],
@@ -306,8 +302,33 @@ class TopazVideoEnhance(IO.ComfyNode):
         target_frame_rate = src_frame_rate
         filters = []
         if upscaler_enabled:
-            target_width = UPSCALER_VALUES_MAP[upscaler_resolution]
+            if "1080p" in upscaler_resolution:
-            target_height = UPSCALER_VALUES_MAP[upscaler_resolution]
+                target_pixel_p = 1080
+                max_long_side = 1920
+            else:
+                target_pixel_p = 2160
+                max_long_side = 3840
+            ar = src_width / src_height
+            if src_width >= src_height:
+                # Landscape or Square; Attempt to set height to target (e.g., 2160), calculate width
+                target_height = target_pixel_p
+                target_width = int(target_height * ar)
+                # Check if width exceeds standard bounds (for ultra-wide e.g., 21:9 ARs)
+                if target_width > max_long_side:
+                    target_width = max_long_side
+                    target_height = int(target_width / ar)
+            else:
+                # Portrait; Attempt to set width to target (e.g., 2160), calculate height
+                target_width = target_pixel_p
+                target_height = int(target_width / ar)
+                # Check if height exceeds standard bounds
+                if target_height > max_long_side:
+                    target_height = max_long_side
+                    target_width = int(target_height * ar)
+            if target_width % 2 != 0:
+                target_width += 1
+            if target_height % 2 != 0:
+                target_height += 1
             filters.append(
                 topaz_api.VideoEnhancementFilter(
                     model=UPSCALER_MODELS_MAP[upscaler_model],

comfy_api_nodes/nodes_tripo.py

@@ -155,7 +155,7 @@ class TripoTextToModelNode(IO.ComfyNode):
                 model_seed=model_seed,
                 texture_seed=texture_seed,
                 texture_quality=texture_quality,
-                face_limit=face_limit,
+                face_limit=face_limit if face_limit != -1 else None,
                 geometry_quality=geometry_quality,
                 auto_size=True,
                 quad=quad,
@@ -255,7 +255,7 @@ class TripoImageToModelNode(IO.ComfyNode):
                 texture_alignment=texture_alignment,
                 texture_seed=texture_seed,
                 texture_quality=texture_quality,
-                face_limit=face_limit,
+                face_limit=face_limit if face_limit != -1 else None,
                 auto_size=True,
                 quad=quad,
             ),
@@ -369,7 +369,7 @@ class TripoMultiviewToModelNode(IO.ComfyNode):
                 texture_quality=texture_quality,
                 geometry_quality=geometry_quality,
                 texture_alignment=texture_alignment,
-                face_limit=face_limit,
+                face_limit=face_limit if face_limit != -1 else None,
                 quad=quad,
             ),
         )

comfy_api_nodes/nodes_veo2.py

@@ -168,6 +168,8 @@ class VeoVideoGenerationNode(IO.ComfyNode):
         # Only add generateAudio for Veo 3 models
         if model.find("veo-2.0") == -1:
             parameters["generateAudio"] = generate_audio
+            # force "enhance_prompt" to True for Veo3 models
+            parameters["enhancePrompt"] = True
 
         initial_response = await sync_op(
             cls,
@@ -291,7 +293,7 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                 IO.Boolean.Input(
                     "enhance_prompt",
                     default=True,
-                    tooltip="Whether to enhance the prompt with AI assistance",
+                    tooltip="This parameter is deprecated and ignored.",
                     optional=True,
                 ),
                 IO.Combo.Input(

comfy_api_nodes/nodes_wan.py

@@ -46,14 +46,14 @@ class Txt2ImageParametersField(BaseModel):
     n: int = Field(1, description="Number of images to generate.")  # we support only value=1
     seed: int = Field(..., ge=0, le=2147483647)
     prompt_extend: bool = Field(True)
-    watermark: bool = Field(True)
+    watermark: bool = Field(False)
 
 
 class Image2ImageParametersField(BaseModel):
     size: str | None = Field(None)
     n: int = Field(1, description="Number of images to generate.")  # we support only value=1
     seed: int = Field(..., ge=0, le=2147483647)
-    watermark: bool = Field(True)
+    watermark: bool = Field(False)
 
 
 class Text2VideoParametersField(BaseModel):
@@ -61,7 +61,7 @@ class Text2VideoParametersField(BaseModel):
     seed: int = Field(..., ge=0, le=2147483647)
     duration: int = Field(5, ge=5, le=15)
     prompt_extend: bool = Field(True)
-    watermark: bool = Field(True)
+    watermark: bool = Field(False)
     audio: bool = Field(False, description="Whether to generate audio automatically.")
     shot_type: str = Field("single")
 
@@ -71,7 +71,7 @@ class Image2VideoParametersField(BaseModel):
     seed: int = Field(..., ge=0, le=2147483647)
     duration: int = Field(5, ge=5, le=15)
     prompt_extend: bool = Field(True)
-    watermark: bool = Field(True)
+    watermark: bool = Field(False)
     audio: bool = Field(False, description="Whether to generate audio automatically.")
     shot_type: str = Field("single")
 
@@ -208,7 +208,7 @@ class WanTextToImageApi(IO.ComfyNode):
                 ),
                 IO.Boolean.Input(
                     "watermark",
-                    default=True,
+                    default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
                 ),
@@ -234,7 +234,7 @@ class WanTextToImageApi(IO.ComfyNode):
         height: int = 1024,
         seed: int = 0,
         prompt_extend: bool = True,
-        watermark: bool = True,
+        watermark: bool = False,
     ):
         initial_response = await sync_op(
             cls,
@@ -327,7 +327,7 @@ class WanImageToImageApi(IO.ComfyNode):
                 ),
                 IO.Boolean.Input(
                     "watermark",
-                    default=True,
+                    default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
                 ),
@@ -353,7 +353,7 @@ class WanImageToImageApi(IO.ComfyNode):
         # width: int = 1024,
         # height: int = 1024,
         seed: int = 0,
-        watermark: bool = True,
+        watermark: bool = False,
     ):
         n_images = get_number_of_images(image)
         if n_images not in (1, 2):
@@ -476,7 +476,7 @@ class WanTextToVideoApi(IO.ComfyNode):
                 ),
                 IO.Boolean.Input(
                     "watermark",
-                    default=True,
+                    default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
                 ),
@@ -512,7 +512,7 @@ class WanTextToVideoApi(IO.ComfyNode):
         seed: int = 0,
         generate_audio: bool = False,
         prompt_extend: bool = True,
-        watermark: bool = True,
+        watermark: bool = False,
         shot_type: str = "single",
     ):
         if "480p" in size and model == "wan2.6-t2v":
@@ -637,7 +637,7 @@ class WanImageToVideoApi(IO.ComfyNode):
                 ),
                 IO.Boolean.Input(
                     "watermark",
-                    default=True,
+                    default=False,
                     tooltip="Whether to add an AI-generated watermark to the result.",
                     optional=True,
                 ),
@@ -674,7 +674,7 @@ class WanImageToVideoApi(IO.ComfyNode):
         seed: int = 0,
         generate_audio: bool = False,
         prompt_extend: bool = True,
-        watermark: bool = True,
+        watermark: bool = False,
         shot_type: str = "single",
     ):
         if get_number_of_images(image) != 1:

comfy_api_nodes/util/_helpers.py

@@ -1,16 +1,22 @@
 import asyncio
 import contextlib
 import os
+import re
 import time
 from collections.abc import Callable
 from io import BytesIO
 
+from yarl import URL
+
 from comfy.cli_args import args
 from comfy.model_management import processing_interrupted
 from comfy_api.latest import IO
 
 from .common_exceptions import ProcessingInterrupted
 
+_HAS_PCT_ESC = re.compile(r"%[0-9A-Fa-f]{2}")  # any % followed by 2 hex digits
+_HAS_BAD_PCT = re.compile(r"%(?![0-9A-Fa-f]{2})")  # any % not followed by 2 hex digits
+
 
 def is_processing_interrupted() -> bool:
     """Return True if user/runtime requested interruption."""
@@ -69,3 +75,17 @@ def get_fs_object_size(path_or_object: str | BytesIO) -> int:
     if isinstance(path_or_object, str):
         return os.path.getsize(path_or_object)
     return len(path_or_object.getvalue())
+
+
+def to_aiohttp_url(url: str) -> URL:
+    """If `url` appears to be already percent-encoded (contains at least one valid %HH
+    escape and no malformed '%' sequences) and contains no raw whitespace/control
+    characters preserve the original encoding byte-for-byte (important for signed/presigned URLs).
+    Otherwise, return `URL(url)` and allow yarl to normalize/quote as needed."""
+    if any(c.isspace() for c in url) or any(ord(c) < 0x20 for c in url):
+        # Avoid encoded=True if URL contains raw whitespace/control chars
+        return URL(url)
+    if _HAS_PCT_ESC.search(url) and not _HAS_BAD_PCT.search(url):
+        # Preserve encoding only if it appears pre-encoded AND has no invalid % sequences
+        return URL(url, encoded=True)
+    return URL(url)

comfy_api_nodes/util/client.py

@@ -430,9 +430,9 @@ def _display_text(
     if status:
         display_lines.append(f"Status: {status.capitalize() if isinstance(status, str) else status}")
     if price is not None:
-        p = f"{float(price):,.4f}".rstrip("0").rstrip(".")
+        p = f"{float(price) * 211:,.1f}".rstrip("0").rstrip(".")
         if p != "0":
-            display_lines.append(f"Price: ${p}")
+            display_lines.append(f"Price: {p} credits")
     if text is not None:
         display_lines.append(text)
     if display_lines:

comfy_api_nodes/util/download_helpers.py

@@ -19,6 +19,7 @@ from ._helpers import (
     get_auth_header,
     is_processing_interrupted,
     sleep_with_interrupt,
+    to_aiohttp_url,
 )
 from .client import _diagnose_connectivity
 from .common_exceptions import ApiServerError, LocalNetworkError, ProcessingInterrupted
@@ -94,7 +95,7 @@ async def download_url_to_bytesio(
 
             monitor_task = asyncio.create_task(_monitor())
 
-            req_task = asyncio.create_task(session.get(url, headers=headers))
+            req_task = asyncio.create_task(session.get(to_aiohttp_url(url), headers=headers))
             done, pending = await asyncio.wait({req_task, monitor_task}, return_when=asyncio.FIRST_COMPLETED)
 
             if monitor_task in done and req_task in pending:

comfy_execution/graph.py

@@ -97,6 +97,11 @@ def get_input_info(
         extra_info = input_info[1]
     else:
         extra_info = {}
+    # if input_type is a list, it is a Combo defined in outdated format; convert it.
+    # NOTE: uncomment this when we are confident old format going away won't cause too much trouble.
+    # if isinstance(input_type, list):
+    #     extra_info["options"] = input_type
+    #     input_type = IO.Combo.io_type
     return input_type, input_category, extra_info
 
 class TopologicalSort:
@@ -202,15 +207,15 @@ class ExecutionList(TopologicalSort):
         return self.output_cache.get(node_id) is not None
 
     def cache_link(self, from_node_id, to_node_id):
-        if not to_node_id in self.execution_cache:
+        if to_node_id not in self.execution_cache:
             self.execution_cache[to_node_id] = {}
         self.execution_cache[to_node_id][from_node_id] = self.output_cache.get(from_node_id)
-        if not from_node_id in self.execution_cache_listeners:
+        if from_node_id not in self.execution_cache_listeners:
             self.execution_cache_listeners[from_node_id] = set()
         self.execution_cache_listeners[from_node_id].add(to_node_id)
 
     def get_cache(self, from_node_id, to_node_id):
-        if not to_node_id in self.execution_cache:
+        if to_node_id not in self.execution_cache:
             return None
         value = self.execution_cache[to_node_id].get(from_node_id)
         if value is None:

comfy_execution/validation.py

@@ -21,14 +21,24 @@ def validate_node_input(
     """
     # If the types are exactly the same, we can return immediately
     # Use pre-union behaviour: inverse of `__ne__`
+    # NOTE: this lets legacy '*' Any types work that override the __ne__ method of the str class.
     if not received_type != input_type:
         return True
 
+    # If one of the types is '*', we can return True immediately; this is the 'Any' type.
+    if received_type == IO.AnyType.io_type or input_type == IO.AnyType.io_type:
+        return True
+
     # If the received type or input_type is a MatchType, we can return True immediately;
     # validation for this is handled by the frontend
     if received_type == IO.MatchType.io_type or input_type == IO.MatchType.io_type:
         return True
 
+    # This accounts for some custom nodes that output lists of options as the type;
+    # if we ever want to break them on purpose, this can be removed
+    if isinstance(received_type, list) and input_type == IO.Combo.io_type:
+        return True
+
     # Not equal, and not strings
     if not isinstance(received_type, str) or not isinstance(input_type, str):
         return False
@@ -37,6 +47,10 @@ def validate_node_input(
     received_types = set(t.strip() for t in received_type.split(","))
     input_types = set(t.strip() for t in input_type.split(","))
 
+    # If any of the types is '*', we can return True immediately; this is the 'Any' type.
+    if IO.AnyType.io_type in received_types or IO.AnyType.io_type in input_types:
+        return True
+
     if strict:
         # In strict mode, all received types must be in the input types
         return received_types.issubset(input_types)

comfy_extras/nodes_apg.py

@@ -55,7 +55,8 @@ class APG(io.ComfyNode):
         def pre_cfg_function(args):
             nonlocal running_avg, prev_sigma
 
-            if len(args["conds_out"]) == 1: return args["conds_out"]
+            if len(args["conds_out"]) == 1:
+                return args["conds_out"]
 
             cond = args["conds_out"][0]
             uncond = args["conds_out"][1]

comfy_extras/nodes_audio.py

@@ -112,7 +112,7 @@ class VAEDecodeAudio(IO.ComfyNode):
         std = torch.std(audio, dim=[1,2], keepdim=True) * 5.0
         std[std < 1.0] = 1.0
         audio /= std
-        return IO.NodeOutput({"waveform": audio, "sample_rate": 44100})
+        return IO.NodeOutput({"waveform": audio, "sample_rate": 44100 if "sample_rate" not in samples else samples["sample_rate"]})
 
     decode = execute  # TODO: remove
 

comfy_extras/nodes_custom_sampler.py

@@ -9,6 +9,7 @@ import comfy.utils
 import node_helpers
 from typing_extensions import override
 from comfy_api.latest import ComfyExtension, io
+import re
 
 
 class BasicScheduler(io.ComfyNode):
@@ -760,8 +761,12 @@ class SamplerCustom(io.ComfyNode):
         out = latent.copy()
         out["samples"] = samples
         if "x0" in x0_output:
+            x0_out = model.model.process_latent_out(x0_output["x0"].cpu())
+            if samples.is_nested:
+                latent_shapes = [x.shape for x in samples.unbind()]
+                x0_out = comfy.nested_tensor.NestedTensor(comfy.utils.unpack_latents(x0_out, latent_shapes))
             out_denoised = latent.copy()
-            out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
+            out_denoised["samples"] = x0_out
         else:
             out_denoised = out
         return io.NodeOutput(out, out_denoised)
@@ -948,8 +953,12 @@ class SamplerCustomAdvanced(io.ComfyNode):
         out = latent.copy()
         out["samples"] = samples
         if "x0" in x0_output:
+            x0_out = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())
+            if samples.is_nested:
+                latent_shapes = [x.shape for x in samples.unbind()]
+                x0_out = comfy.nested_tensor.NestedTensor(comfy.utils.unpack_latents(x0_out, latent_shapes))
             out_denoised = latent.copy()
-            out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())
+            out_denoised["samples"] = x0_out
         else:
             out_denoised = out
         return io.NodeOutput(out, out_denoised)
@@ -1005,6 +1014,25 @@ class AddNoise(io.ComfyNode):
 
     add_noise = execute
 
+class ManualSigmas(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ManualSigmas",
+            category="_for_testing/custom_sampling",
+            is_experimental=True,
+            inputs=[
+                io.String.Input("sigmas", default="1, 0.5", multiline=False)
+            ],
+            outputs=[io.Sigmas.Output()]
+        )
+
+    @classmethod
+    def execute(cls, sigmas) -> io.NodeOutput:
+        sigmas = re.findall(r"[-+]?(?:\d*\.*\d+)", sigmas)
+        sigmas = [float(i) for i in sigmas]
+        sigmas = torch.FloatTensor(sigmas)
+        return io.NodeOutput(sigmas)
 
 class CustomSamplersExtension(ComfyExtension):
     @override
@@ -1044,6 +1072,7 @@ class CustomSamplersExtension(ComfyExtension):
             DisableNoise,
             AddNoise,
             SamplerCustomAdvanced,
+            ManualSigmas,
         ]
 
 

comfy_extras/nodes_dataset.py

@@ -667,16 +667,19 @@ class ResizeImagesByLongerEdgeNode(ImageProcessingNode):
 
     @classmethod
     def _process(cls, image, longer_edge):
-        img = tensor_to_pil(image)
+        resized_images = []
-        w, h = img.size
+        for image_i in image:
-        if w > h:
+            img = tensor_to_pil(image_i)
-            new_w = longer_edge
+            w, h = img.size
-            new_h = int(h * (longer_edge / w))
+            if w > h:
-        else:
+                new_w = longer_edge
-            new_h = longer_edge
+                new_h = int(h * (longer_edge / w))
-            new_w = int(w * (longer_edge / h))
+            else:
-        img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
+                new_h = longer_edge
-        return pil_to_tensor(img)
+                new_w = int(w * (longer_edge / h))
+            img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
+            resized_images.append(pil_to_tensor(img))
+        return torch.cat(resized_images, dim=0)
 
 
 class CenterCropImagesNode(ImageProcessingNode):

comfy_extras/nodes_hunyuan.py

@@ -5,7 +5,9 @@ import comfy.model_management
 from typing_extensions import override
 from comfy_api.latest import ComfyExtension, io
 from comfy.ldm.hunyuan_video.upsampler import HunyuanVideo15SRModel
+from comfy.ldm.lightricks.latent_upsampler import LatentUpsampler
 import folder_paths
+import json
 
 class CLIPTextEncodeHunyuanDiT(io.ComfyNode):
     @classmethod
@@ -186,7 +188,7 @@ class LatentUpscaleModelLoader(io.ComfyNode):
     @classmethod
     def execute(cls, model_name) -> io.NodeOutput:
         model_path = folder_paths.get_full_path_or_raise("latent_upscale_models", model_name)
-        sd = comfy.utils.load_torch_file(model_path, safe_load=True)
+        sd, metadata = comfy.utils.load_torch_file(model_path, safe_load=True, return_metadata=True)
 
         if "blocks.0.block.0.conv.weight" in sd:
             config = {
@@ -197,6 +199,8 @@ class LatentUpscaleModelLoader(io.ComfyNode):
                 "global_residual": False,
             }
             model_type = "720p"
+            model = HunyuanVideo15SRModel(model_type, config)
+            model.load_sd(sd)
         elif "up.0.block.0.conv1.conv.weight" in sd:
             sd = {key.replace("nin_shortcut", "nin_shortcut.conv", 1): value for key, value in sd.items()}
             config = {
@@ -205,9 +209,12 @@ class LatentUpscaleModelLoader(io.ComfyNode):
                 "block_out_channels": tuple(sd[f"up.{i}.block.0.conv1.conv.weight"].shape[0] for i in range(len([k for k in sd.keys() if k.startswith("up.") and k.endswith(".block.0.conv1.conv.weight")]))),
             }
             model_type = "1080p"
-
+            model = HunyuanVideo15SRModel(model_type, config)
-        model = HunyuanVideo15SRModel(model_type, config)
+            model.load_sd(sd)
-        model.load_sd(sd)
+        elif "post_upsample_res_blocks.0.conv2.bias" in sd:
+            config = json.loads(metadata["config"])
+            model = LatentUpsampler.from_config(config).to(dtype=comfy.model_management.vae_dtype(allowed_dtypes=[torch.bfloat16, torch.float32]))
+            model.load_state_dict(sd)
 
         return io.NodeOutput(model)
 

comfy_extras/nodes_images.py

@@ -2,280 +2,231 @@ from __future__ import annotations
 
 import nodes
 import folder_paths
-from comfy.cli_args import args
 
-from PIL import Image
-from PIL.PngImagePlugin import PngInfo
-
-import numpy as np
 import json
 import os
 import re
-from io import BytesIO
-from inspect import cleandoc
 import torch
 import comfy.utils
 
-from comfy.comfy_types import FileLocator, IO
 from server import PromptServer
+from comfy_api.latest import ComfyExtension, IO, UI
+from typing_extensions import override
+
+SVG = IO.SVG.Type  # TODO: temporary solution for backward compatibility, will be removed later.
 
 MAX_RESOLUTION = nodes.MAX_RESOLUTION
 
-class ImageCrop:
+class ImageCrop(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": { "image": ("IMAGE",),
+        return IO.Schema(
-                              "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+            node_id="ImageCrop",
-                              "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+            display_name="Image Crop",
-                              "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+            category="image/transform",
-                              "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+            inputs=[
-                              }}
+                IO.Image.Input("image"),
-    RETURN_TYPES = ("IMAGE",)
+                IO.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
-    FUNCTION = "crop"
+                IO.Int.Input("height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+            ],
+            outputs=[IO.Image.Output()],
+        )
 
-    CATEGORY = "image/transform"
+    @classmethod
-
+    def execute(cls, image, width, height, x, y) -> IO.NodeOutput:
-    def crop(self, image, width, height, x, y):
         x = min(x, image.shape[2] - 1)
         y = min(y, image.shape[1] - 1)
         to_x = width + x
         to_y = height + y
         img = image[:,y:to_y, x:to_x, :]
-        return (img,)
+        return IO.NodeOutput(img)
 
-class RepeatImageBatch:
+    crop = execute  # TODO: remove
+
+
+class RepeatImageBatch(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": { "image": ("IMAGE",),
+        return IO.Schema(
-                              "amount": ("INT", {"default": 1, "min": 1, "max": 4096}),
+            node_id="RepeatImageBatch",
-                              }}
+            category="image/batch",
-    RETURN_TYPES = ("IMAGE",)
+            inputs=[
-    FUNCTION = "repeat"
+                IO.Image.Input("image"),
+                IO.Int.Input("amount", default=1, min=1, max=4096),
+            ],
+            outputs=[IO.Image.Output()],
+        )
 
-    CATEGORY = "image/batch"
+    @classmethod
-
+    def execute(cls, image, amount) -> IO.NodeOutput:
-    def repeat(self, image, amount):
         s = image.repeat((amount, 1,1,1))
-        return (s,)
+        return IO.NodeOutput(s)
 
-class ImageFromBatch:
+    repeat = execute  # TODO: remove
+
+
+class ImageFromBatch(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": { "image": ("IMAGE",),
+        return IO.Schema(
-                              "batch_index": ("INT", {"default": 0, "min": 0, "max": 4095}),
+            node_id="ImageFromBatch",
-                              "length": ("INT", {"default": 1, "min": 1, "max": 4096}),
+            category="image/batch",
-                              }}
+            inputs=[
-    RETURN_TYPES = ("IMAGE",)
+                IO.Image.Input("image"),
-    FUNCTION = "frombatch"
+                IO.Int.Input("batch_index", default=0, min=0, max=4095),
+                IO.Int.Input("length", default=1, min=1, max=4096),
+            ],
+            outputs=[IO.Image.Output()],
+        )
 
-    CATEGORY = "image/batch"
+    @classmethod
-
+    def execute(cls, image, batch_index, length) -> IO.NodeOutput:
-    def frombatch(self, image, batch_index, length):
         s_in = image
         batch_index = min(s_in.shape[0] - 1, batch_index)
         length = min(s_in.shape[0] - batch_index, length)
         s = s_in[batch_index:batch_index + length].clone()
-        return (s,)
+        return IO.NodeOutput(s)
+
+    frombatch = execute  # TODO: remove
 
 
-class ImageAddNoise:
+class ImageAddNoise(IO.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": { "image": ("IMAGE",),
+        return IO.Schema(
-                              "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}),
+            node_id="ImageAddNoise",
-                              "strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+            category="image",
-                              }}
+            inputs=[
-    RETURN_TYPES = ("IMAGE",)
+                IO.Image.Input("image"),
-    FUNCTION = "repeat"
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+                IO.Float.Input("strength", default=0.5, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[IO.Image.Output()],
+        )
 
-    CATEGORY = "image"
+    @classmethod
-
+    def execute(cls, image, seed, strength) -> IO.NodeOutput:
-    def repeat(self, image, seed, strength):
         generator = torch.manual_seed(seed)
         s = torch.clip((image + strength * torch.randn(image.size(), generator=generator, device="cpu").to(image)), min=0.0, max=1.0)
-        return (s,)
+        return IO.NodeOutput(s)
 
-class SaveAnimatedWEBP:
+    repeat = execute  # TODO: remove
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "output"
-        self.prefix_append = ""
 
-    methods = {"default": 4, "fastest": 0, "slowest": 6}
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {"images": ("IMAGE", ),
-                     "filename_prefix": ("STRING", {"default": "ComfyUI"}),
-                     "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
-                     "lossless": ("BOOLEAN", {"default": True}),
-                     "quality": ("INT", {"default": 80, "min": 0, "max": 100}),
-                     "method": (list(s.methods.keys()),),
-                     # "num_frames": ("INT", {"default": 0, "min": 0, "max": 8192}),
-                     },
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
-                }
 
-    RETURN_TYPES = ()
+class SaveAnimatedWEBP(IO.ComfyNode):
-    FUNCTION = "save_images"
+    COMPRESS_METHODS = {"default": 4, "fastest": 0, "slowest": 6}
-
-    OUTPUT_NODE = True
-
-    CATEGORY = "image/animation"
-
-    def save_images(self, images, fps, filename_prefix, lossless, quality, method, num_frames=0, prompt=None, extra_pnginfo=None):
-        method = self.methods.get(method)
-        filename_prefix += self.prefix_append
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
-        results: list[FileLocator] = []
-        pil_images = []
-        for image in images:
-            i = 255. * image.cpu().numpy()
-            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
-            pil_images.append(img)
-
-        metadata = pil_images[0].getexif()
-        if not args.disable_metadata:
-            if prompt is not None:
-                metadata[0x0110] = "prompt:{}".format(json.dumps(prompt))
-            if extra_pnginfo is not None:
-                inital_exif = 0x010f
-                for x in extra_pnginfo:
-                    metadata[inital_exif] = "{}:{}".format(x, json.dumps(extra_pnginfo[x]))
-                    inital_exif -= 1
-
-        if num_frames == 0:
-            num_frames = len(pil_images)
-
-        c = len(pil_images)
-        for i in range(0, c, num_frames):
-            file = f"{filename}_{counter:05}_.webp"
-            pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], exif=metadata, lossless=lossless, quality=quality, method=method)
-            results.append({
-                "filename": file,
-                "subfolder": subfolder,
-                "type": self.type
-            })
-            counter += 1
-
-        animated = num_frames != 1
-        return { "ui": { "images": results, "animated": (animated,) } }
-
-class SaveAnimatedPNG:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "output"
-        self.prefix_append = ""
 
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required":
+        return IO.Schema(
-                    {"images": ("IMAGE", ),
+            node_id="SaveAnimatedWEBP",
-                     "filename_prefix": ("STRING", {"default": "ComfyUI"}),
+            category="image/animation",
-                     "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
+            inputs=[
-                     "compress_level": ("INT", {"default": 4, "min": 0, "max": 9})
+                IO.Image.Input("images"),
-                     },
+                IO.String.Input("filename_prefix", default="ComfyUI"),
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                IO.Float.Input("fps", default=6.0, min=0.01, max=1000.0, step=0.01),
-                }
+                IO.Boolean.Input("lossless", default=True),
+                IO.Int.Input("quality", default=80, min=0, max=100),
+                IO.Combo.Input("method", options=list(cls.COMPRESS_METHODS.keys())),
+                # "num_frames": ("INT", {"default": 0, "min": 0, "max": 8192}),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
 
-    RETURN_TYPES = ()
+    @classmethod
-    FUNCTION = "save_images"
+    def execute(cls, images, fps, filename_prefix, lossless, quality, method, num_frames=0) -> IO.NodeOutput:
+        return IO.NodeOutput(
+            ui=UI.ImageSaveHelper.get_save_animated_webp_ui(
+                images=images,
+                filename_prefix=filename_prefix,
+                cls=cls,
+                fps=fps,
+                lossless=lossless,
+                quality=quality,
+                method=cls.COMPRESS_METHODS.get(method)
+            )
+        )
 
-    OUTPUT_NODE = True
+    save_images = execute  # TODO: remove
-
-    CATEGORY = "image/animation"
-
-    def save_images(self, images, fps, compress_level, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
-        filename_prefix += self.prefix_append
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
-        results = list()
-        pil_images = []
-        for image in images:
-            i = 255. * image.cpu().numpy()
-            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
-            pil_images.append(img)
-
-        metadata = None
-        if not args.disable_metadata:
-            metadata = PngInfo()
-            if prompt is not None:
-                metadata.add(b"comf", "prompt".encode("latin-1", "strict") + b"\0" + json.dumps(prompt).encode("latin-1", "strict"), after_idat=True)
-            if extra_pnginfo is not None:
-                for x in extra_pnginfo:
-                    metadata.add(b"comf", x.encode("latin-1", "strict") + b"\0" + json.dumps(extra_pnginfo[x]).encode("latin-1", "strict"), after_idat=True)
-
-        file = f"{filename}_{counter:05}_.png"
-        pil_images[0].save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level, save_all=True, duration=int(1000.0/fps), append_images=pil_images[1:])
-        results.append({
-            "filename": file,
-            "subfolder": subfolder,
-            "type": self.type
-        })
-
-        return { "ui": { "images": results, "animated": (True,)} }
-
-class SVG:
-    """
-    Stores SVG representations via a list of BytesIO objects.
-    """
-    def __init__(self, data: list[BytesIO]):
-        self.data = data
-
-    def combine(self, other: 'SVG') -> 'SVG':
-        return SVG(self.data + other.data)
-
-    @staticmethod
-    def combine_all(svgs: list['SVG']) -> 'SVG':
-        all_svgs_list: list[BytesIO] = []
-        for svg_item in svgs:
-            all_svgs_list.extend(svg_item.data)
-        return SVG(all_svgs_list)
 
 
-class ImageStitch:
+class SaveAnimatedPNG(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveAnimatedPNG",
+            category="image/animation",
+            inputs=[
+                IO.Image.Input("images"),
+                IO.String.Input("filename_prefix", default="ComfyUI"),
+                IO.Float.Input("fps", default=6.0, min=0.01, max=1000.0, step=0.01),
+                IO.Int.Input("compress_level", default=4, min=0, max=9),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, images, fps, compress_level, filename_prefix="ComfyUI") -> IO.NodeOutput:
+        return IO.NodeOutput(
+            ui=UI.ImageSaveHelper.get_save_animated_png_ui(
+                images=images,
+                filename_prefix=filename_prefix,
+                cls=cls,
+                fps=fps,
+                compress_level=compress_level,
+            )
+        )
+
+    save_images = execute  # TODO: remove
+
+
+class ImageStitch(IO.ComfyNode):
     """Upstreamed from https://github.com/kijai/ComfyUI-KJNodes"""
 
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {
+        return IO.Schema(
-            "required": {
+            node_id="ImageStitch",
-                "image1": ("IMAGE",),
+            display_name="Image Stitch",
-                "direction": (["right", "down", "left", "up"], {"default": "right"}),
+            description="Stitches image2 to image1 in the specified direction.\n"
-                "match_image_size": ("BOOLEAN", {"default": True}),
+            "If image2 is not provided, returns image1 unchanged.\n"
-                "spacing_width": (
+            "Optional spacing can be added between images.",
-                    "INT",
+            category="image/transform",
-                    {"default": 0, "min": 0, "max": 1024, "step": 2},
+            inputs=[
-                ),
+                IO.Image.Input("image1"),
-                "spacing_color": (
+                IO.Combo.Input("direction", options=["right", "down", "left", "up"], default="right"),
-                    ["white", "black", "red", "green", "blue"],
+                IO.Boolean.Input("match_image_size", default=True),
-                    {"default": "white"},
+                IO.Int.Input("spacing_width", default=0, min=0, max=1024, step=2),
-                ),
+                IO.Combo.Input("spacing_color", options=["white", "black", "red", "green", "blue"], default="white"),
-            },
+                IO.Image.Input("image2", optional=True),
-            "optional": {
+            ],
-                "image2": ("IMAGE",),
+            outputs=[IO.Image.Output()],
-            },
+        )
-        }
 
-    RETURN_TYPES = ("IMAGE",)
+    @classmethod
-    FUNCTION = "stitch"
+    def execute(
-    CATEGORY = "image/transform"
+        cls,
-    DESCRIPTION = """
-Stitches image2 to image1 in the specified direction.
-If image2 is not provided, returns image1 unchanged.
-Optional spacing can be added between images.
-"""
-
-    def stitch(
-        self,
         image1,
         direction,
         match_image_size,
         spacing_width,
         spacing_color,
         image2=None,
-    ):
+    ) -> IO.NodeOutput:
         if image2 is None:
-            return (image1,)
+            return IO.NodeOutput(image1)
 
         # Handle batch size differences
         if image1.shape[0] != image2.shape[0]:
@@ -412,36 +363,30 @@ Optional spacing can be added between images.
             images.insert(1, spacing)
 
         concat_dim = 2 if direction in ["left", "right"] else 1
-        return (torch.cat(images, dim=concat_dim),)
+        return IO.NodeOutput(torch.cat(images, dim=concat_dim))
+
+    stitch = execute  # TODO: remove
+
+
+class ResizeAndPadImage(IO.ComfyNode):
 
-class ResizeAndPadImage:
     @classmethod
-    def INPUT_TYPES(cls):
+    def define_schema(cls):
-        return {
+        return IO.Schema(
-            "required": {
+            node_id="ResizeAndPadImage",
-                "image": ("IMAGE",),
+            category="image/transform",
-                "target_width": ("INT", {
+            inputs=[
-                    "default": 512,
+                IO.Image.Input("image"),
-                    "min": 1,
+                IO.Int.Input("target_width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
-                    "max": MAX_RESOLUTION,
+                IO.Int.Input("target_height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
-                    "step": 1
+                IO.Combo.Input("padding_color", options=["white", "black"]),
-                }),
+                IO.Combo.Input("interpolation", options=["area", "bicubic", "nearest-exact", "bilinear", "lanczos"]),
-                "target_height": ("INT", {
+            ],
-                    "default": 512,
+            outputs=[IO.Image.Output()],
-                    "min": 1,
+        )
-                    "max": MAX_RESOLUTION,
-                    "step": 1
-                }),
-                "padding_color": (["white", "black"],),
-                "interpolation": (["area", "bicubic", "nearest-exact", "bilinear", "lanczos"],),
-            }
-        }
 
-    RETURN_TYPES = ("IMAGE",)
+    @classmethod
-    FUNCTION = "resize_and_pad"
+    def execute(cls, image, target_width, target_height, padding_color, interpolation) -> IO.NodeOutput:
-    CATEGORY = "image/transform"
-
-    def resize_and_pad(self, image, target_width, target_height, padding_color, interpolation):
         batch_size, orig_height, orig_width, channels = image.shape
 
         scale_w = target_width / orig_width
@@ -469,52 +414,47 @@ class ResizeAndPadImage:
         padded[:, :, y_offset:y_offset + new_height, x_offset:x_offset + new_width] = resized
 
         output = padded.permute(0, 2, 3, 1)
-        return (output,)
+        return IO.NodeOutput(output)
 
-class SaveSVGNode:
+    resize_and_pad = execute  # TODO: remove
-    """
-    Save SVG files on disk.
-    """
 
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "output"
-        self.prefix_append = ""
 
-    RETURN_TYPES = ()
+class SaveSVGNode(IO.ComfyNode):
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "save_svg"
-    CATEGORY = "image/save" # Changed
-    OUTPUT_NODE = True
 
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {
+        return IO.Schema(
-            "required": {
+            node_id="SaveSVGNode",
-                "svg": ("SVG",), # Changed
+            description="Save SVG files on disk.",
-                "filename_prefix": ("STRING", {"default": "svg/ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."})
+            category="image/save",
-            },
+            inputs=[
-            "hidden": {
+                IO.SVG.Input("svg"),
-                "prompt": "PROMPT",
+                IO.String.Input(
-                "extra_pnginfo": "EXTRA_PNGINFO"
+                    "filename_prefix",
-            }
+                    default="svg/ComfyUI",
-        }
+                    tooltip="The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
+                ),
+            ],
+            hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
 
-    def save_svg(self, svg: SVG, filename_prefix="svg/ComfyUI", prompt=None, extra_pnginfo=None):
+    @classmethod
-        filename_prefix += self.prefix_append
+    def execute(cls, svg: IO.SVG.Type, filename_prefix="svg/ComfyUI") -> IO.NodeOutput:
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory())
-        results = list()
+        results: list[UI.SavedResult] = []
 
         # Prepare metadata JSON
         metadata_dict = {}
-        if prompt is not None:
+        if cls.hidden.prompt is not None:
-            metadata_dict["prompt"] = prompt
+            metadata_dict["prompt"] = cls.hidden.prompt
-        if extra_pnginfo is not None:
+        if cls.hidden.extra_pnginfo is not None:
-            metadata_dict.update(extra_pnginfo)
+            metadata_dict.update(cls.hidden.extra_pnginfo)
 
         # Convert metadata to JSON string
         metadata_json = json.dumps(metadata_dict, indent=2) if metadata_dict else None
 
+
         for batch_number, svg_bytes in enumerate(svg.data):
             filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
             file = f"{filename_with_batch_num}_{counter:05}_.svg"
@@ -544,57 +484,64 @@ class SaveSVGNode:
             with open(os.path.join(full_output_folder, file), 'wb') as svg_file:
                 svg_file.write(svg_content.encode('utf-8'))
 
-            results.append({
+            results.append(UI.SavedResult(filename=file, subfolder=subfolder, type=IO.FolderType.output))
-                "filename": file,
-                "subfolder": subfolder,
-                "type": self.type
-            })
             counter += 1
-        return { "ui": { "images": results } }
+        return IO.NodeOutput(ui={"images": results})
 
-class GetImageSize:
+    save_svg = execute  # TODO: remove
+
+
+class GetImageSize(IO.ComfyNode):
 
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {
+        return IO.Schema(
-            "required": {
+            node_id="GetImageSize",
-                "image": (IO.IMAGE,),
+            display_name="Get Image Size",
-            },
+            description="Returns width and height of the image, and passes it through unchanged.",
-            "hidden": {
+            category="image",
-                "unique_id": "UNIQUE_ID",
+            inputs=[
-            }
+                IO.Image.Input("image"),
-        }
+            ],
+            outputs=[
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+                IO.Int.Output(display_name="batch_size"),
+            ],
+            hidden=[IO.Hidden.unique_id],
+        )
 
-    RETURN_TYPES = (IO.INT, IO.INT, IO.INT)
+    @classmethod
-    RETURN_NAMES = ("width", "height", "batch_size")
+    def execute(cls, image) -> IO.NodeOutput:
-    FUNCTION = "get_size"
-
-    CATEGORY = "image"
-    DESCRIPTION = """Returns width and height of the image, and passes it through unchanged."""
-
-    def get_size(self, image, unique_id=None) -> tuple[int, int]:
         height = image.shape[1]
         width = image.shape[2]
         batch_size = image.shape[0]
 
         # Send progress text to display size on the node
-        if unique_id:
+        if cls.hidden.unique_id:
-            PromptServer.instance.send_progress_text(f"width: {width}, height: {height}\n batch size: {batch_size}", unique_id)
+            PromptServer.instance.send_progress_text(f"width: {width}, height: {height}\n batch size: {batch_size}", cls.hidden.unique_id)
 
-        return width, height, batch_size
+        return IO.NodeOutput(width, height, batch_size)
+
+    get_size = execute  # TODO: remove
+
+
+class ImageRotate(IO.ComfyNode):
 
-class ImageRotate:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": { "image": (IO.IMAGE,),
+        return IO.Schema(
-                              "rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],),
+            node_id="ImageRotate",
-                              }}
+            category="image/transform",
-    RETURN_TYPES = (IO.IMAGE,)
+            inputs=[
-    FUNCTION = "rotate"
+                IO.Image.Input("image"),
+                IO.Combo.Input("rotation", options=["none", "90 degrees", "180 degrees", "270 degrees"]),
+            ],
+            outputs=[IO.Image.Output()],
+        )
 
-    CATEGORY = "image/transform"
+    @classmethod
-
+    def execute(cls, image, rotation) -> IO.NodeOutput:
-    def rotate(self, image, rotation):
         rotate_by = 0
         if rotation.startswith("90"):
             rotate_by = 1
@@ -604,41 +551,57 @@ class ImageRotate:
             rotate_by = 3
 
         image = torch.rot90(image, k=rotate_by, dims=[2, 1])
-        return (image,)
+        return IO.NodeOutput(image)
+
+    rotate = execute  # TODO: remove
+
+
+class ImageFlip(IO.ComfyNode):
 
-class ImageFlip:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": { "image": (IO.IMAGE,),
+        return IO.Schema(
-                              "flip_method": (["x-axis: vertically", "y-axis: horizontally"],),
+            node_id="ImageFlip",
-                              }}
+            category="image/transform",
-    RETURN_TYPES = (IO.IMAGE,)
+            inputs=[
-    FUNCTION = "flip"
+                IO.Image.Input("image"),
+                IO.Combo.Input("flip_method", options=["x-axis: vertically", "y-axis: horizontally"]),
+            ],
+            outputs=[IO.Image.Output()],
+        )
 
-    CATEGORY = "image/transform"
+    @classmethod
-
+    def execute(cls, image, flip_method) -> IO.NodeOutput:
-    def flip(self, image, flip_method):
         if flip_method.startswith("x"):
             image = torch.flip(image, dims=[1])
         elif flip_method.startswith("y"):
             image = torch.flip(image, dims=[2])
 
-        return (image,)
+        return IO.NodeOutput(image)
 
-class ImageScaleToMaxDimension:
+    flip = execute  # TODO: remove
-    upscale_methods = ["area", "lanczos", "bilinear", "nearest-exact", "bilinear", "bicubic"]
+
+
+class ImageScaleToMaxDimension(IO.ComfyNode):
 
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": {"image": ("IMAGE",),
+        return IO.Schema(
-                             "upscale_method": (s.upscale_methods,),
+            node_id="ImageScaleToMaxDimension",
-                             "largest_size": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1})}}
+            category="image/upscaling",
-    RETURN_TYPES = ("IMAGE",)
+            inputs=[
-    FUNCTION = "upscale"
+                IO.Image.Input("image"),
+                IO.Combo.Input(
+                    "upscale_method",
+                    options=["area", "lanczos", "bilinear", "nearest-exact", "bilinear", "bicubic"],
+                ),
+                IO.Int.Input("largest_size", default=512, min=0, max=MAX_RESOLUTION, step=1),
+            ],
+            outputs=[IO.Image.Output()],
+        )
 
-    CATEGORY = "image/upscaling"
+    @classmethod
-
+    def execute(cls, image, upscale_method, largest_size) -> IO.NodeOutput:
-    def upscale(self, image, upscale_method, largest_size):
         height = image.shape[1]
         width = image.shape[2]
 
@@ -655,20 +618,30 @@ class ImageScaleToMaxDimension:
         samples = image.movedim(-1, 1)
         s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
         s = s.movedim(1, -1)
-        return (s,)
+        return IO.NodeOutput(s)
 
-NODE_CLASS_MAPPINGS = {
+    upscale = execute    # TODO: remove
-    "ImageCrop": ImageCrop,
+
-    "RepeatImageBatch": RepeatImageBatch,
+
-    "ImageFromBatch": ImageFromBatch,
+class ImagesExtension(ComfyExtension):
-    "ImageAddNoise": ImageAddNoise,
+    @override
-    "SaveAnimatedWEBP": SaveAnimatedWEBP,
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
-    "SaveAnimatedPNG": SaveAnimatedPNG,
+        return [
-    "SaveSVGNode": SaveSVGNode,
+            ImageCrop,
-    "ImageStitch": ImageStitch,
+            RepeatImageBatch,
-    "ResizeAndPadImage": ResizeAndPadImage,
+            ImageFromBatch,
-    "GetImageSize": GetImageSize,
+            ImageAddNoise,
-    "ImageRotate": ImageRotate,
+            SaveAnimatedWEBP,
-    "ImageFlip": ImageFlip,
+            SaveAnimatedPNG,
-    "ImageScaleToMaxDimension": ImageScaleToMaxDimension,
+            SaveSVGNode,
-}
+            ImageStitch,
+            ResizeAndPadImage,
+            GetImageSize,
+            ImageRotate,
+            ImageFlip,
+            ImageScaleToMaxDimension,
+        ]
+
+
+async def comfy_entrypoint() -> ImagesExtension:
+    return ImagesExtension()

comfy_extras/nodes_latent.py

@@ -255,6 +255,7 @@ class LatentBatch(io.ComfyNode):
         return io.Schema(
             node_id="LatentBatch",
             category="latent/batch",
+            is_deprecated=True,
             inputs=[
                 io.Latent.Input("samples1"),
                 io.Latent.Input("samples2"),

comfy_extras/nodes_logic.py

@@ -1,8 +1,11 @@
+from __future__ import annotations
 from typing import TypedDict
 from typing_extensions import override
 from comfy_api.latest import ComfyExtension, io
 from comfy_api.latest import _io
 
+# sentinel for missing inputs
+MISSING = object()
 
 
 class SwitchNode(io.ComfyNode):
@@ -14,6 +17,37 @@ class SwitchNode(io.ComfyNode):
             display_name="Switch",
             category="logic",
             is_experimental=True,
+            inputs=[
+                io.Boolean.Input("switch"),
+                io.MatchType.Input("on_false", template=template, lazy=True),
+                io.MatchType.Input("on_true", template=template, lazy=True),
+            ],
+            outputs=[
+                io.MatchType.Output(template=template, display_name="output"),
+            ],
+        )
+
+    @classmethod
+    def check_lazy_status(cls, switch, on_false=None, on_true=None):
+        if switch and on_true is None:
+            return ["on_true"]
+        if not switch and on_false is None:
+            return ["on_false"]
+
+    @classmethod
+    def execute(cls, switch, on_true, on_false) -> io.NodeOutput:
+        return io.NodeOutput(on_true if switch else on_false)
+
+
+class SoftSwitchNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        template = io.MatchType.Template("switch")
+        return io.Schema(
+            node_id="ComfySoftSwitchNode",
+            display_name="Soft Switch",
+            category="logic",
+            is_experimental=True,
             inputs=[
                 io.Boolean.Input("switch"),
                 io.MatchType.Input("on_false", template=template, lazy=True, optional=True),
@@ -25,14 +59,14 @@ class SwitchNode(io.ComfyNode):
         )
 
     @classmethod
-    def check_lazy_status(cls, switch, on_false=..., on_true=...):
+    def check_lazy_status(cls, switch, on_false=MISSING, on_true=MISSING):
-        # We use ... instead of None, as None is passed for connected-but-unevaluated inputs.
+        # We use MISSING instead of None, as None is passed for connected-but-unevaluated inputs.
         # This trick allows us to ignore the value of the switch and still be able to run execute().
 
         # One of the inputs may be missing, in which case we need to evaluate the other input
-        if on_false is ...:
+        if on_false is MISSING:
             return ["on_true"]
-        if on_true is ...:
+        if on_true is MISSING:
             return ["on_false"]
         # Normal lazy switch operation
         if switch and on_true is None:
@@ -41,22 +75,50 @@ class SwitchNode(io.ComfyNode):
             return ["on_false"]
 
     @classmethod
-    def validate_inputs(cls, switch, on_false=..., on_true=...):
+    def validate_inputs(cls, switch, on_false=MISSING, on_true=MISSING):
         # This check happens before check_lazy_status(), so we can eliminate the case where
         # both inputs are missing.
-        if on_false is ... and on_true is ...:
+        if on_false is MISSING and on_true is MISSING:
             return "At least one of on_false or on_true must be connected to Switch node"
         return True
 
     @classmethod
-    def execute(cls, switch, on_true=..., on_false=...) -> io.NodeOutput:
+    def execute(cls, switch, on_true=MISSING, on_false=MISSING) -> io.NodeOutput:
-        if on_true is ...:
+        if on_true is MISSING:
             return io.NodeOutput(on_false)
-        if on_false is ...:
+        if on_false is MISSING:
             return io.NodeOutput(on_true)
         return io.NodeOutput(on_true if switch else on_false)
 
 
+class CustomComboNode(io.ComfyNode):
+    """
+    Frontend node that allows user to write their own options for a combo.
+    This is here to make sure the node has a backend-representation to avoid some annoyances.
+    """
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CustomCombo",
+            display_name="Custom Combo",
+            category="utils",
+            is_experimental=True,
+            inputs=[io.Combo.Input("choice", options=[])],
+            outputs=[io.String.Output()]
+        )
+
+    @classmethod
+    def validate_inputs(cls, choice: io.Combo.Type) -> bool:
+        # NOTE: DO NOT DO THIS unless you want to skip validation entirely on the node's inputs.
+        # I am doing that here because the widgets (besides the combo dropdown) on this node are fully frontend defined.
+        # I need to skip checking that the chosen combo option is in the options list, since those are defined by the user.
+        return True
+
+    @classmethod
+    def execute(cls, choice: io.Combo.Type) -> io.NodeOutput:
+        return io.NodeOutput(choice)
+
+
 class DCTestNode(io.ComfyNode):
     class DCValues(TypedDict):
         combo: str
@@ -72,14 +134,14 @@ class DCTestNode(io.ComfyNode):
             display_name="DCTest",
             category="logic",
             is_output_node=True,
-            inputs=[_io.DynamicCombo.Input("combo", options=[
+            inputs=[io.DynamicCombo.Input("combo", options=[
-                _io.DynamicCombo.Option("option1", [io.String.Input("string")]),
+                io.DynamicCombo.Option("option1", [io.String.Input("string")]),
-                _io.DynamicCombo.Option("option2", [io.Int.Input("integer")]),
+                io.DynamicCombo.Option("option2", [io.Int.Input("integer")]),
-                _io.DynamicCombo.Option("option3", [io.Image.Input("image")]),
+                io.DynamicCombo.Option("option3", [io.Image.Input("image")]),
-                _io.DynamicCombo.Option("option4", [
+                io.DynamicCombo.Option("option4", [
-                    _io.DynamicCombo.Input("subcombo", options=[
+                    io.DynamicCombo.Input("subcombo", options=[
-                        _io.DynamicCombo.Option("opt1", [io.Float.Input("float_x"), io.Float.Input("float_y")]),
+                        io.DynamicCombo.Option("opt1", [io.Float.Input("float_x"), io.Float.Input("float_y")]),
-                        _io.DynamicCombo.Option("opt2", [io.Mask.Input("mask1", optional=True)]),
+                        io.DynamicCombo.Option("opt2", [io.Mask.Input("mask1", optional=True)]),
                     ])
                 ])]
             )],
@@ -141,14 +203,65 @@ class AutogrowPrefixTestNode(io.ComfyNode):
         combined = ",".join([str(x) for x in vals])
         return io.NodeOutput(combined)
 
+class ComboOutputTestNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ComboOptionTestNode",
+            display_name="ComboOptionTest",
+            category="logic",
+            inputs=[io.Combo.Input("combo", options=["option1", "option2", "option3"]),
+                    io.Combo.Input("combo2", options=["option4", "option5", "option6"])],
+            outputs=[io.Combo.Output(), io.Combo.Output()],
+        )
+
+    @classmethod
+    def execute(cls, combo: io.Combo.Type, combo2: io.Combo.Type) -> io.NodeOutput:
+        return io.NodeOutput(combo, combo2)
+
+class ConvertStringToComboNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ConvertStringToComboNode",
+            display_name="Convert String to Combo",
+            category="logic",
+            inputs=[io.String.Input("string")],
+            outputs=[io.Combo.Output()],
+        )
+
+    @classmethod
+    def execute(cls, string: str) -> io.NodeOutput:
+        return io.NodeOutput(string)
+
+class InvertBooleanNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="InvertBooleanNode",
+            display_name="Invert Boolean",
+            category="logic",
+            inputs=[io.Boolean.Input("boolean")],
+            outputs=[io.Boolean.Output()],
+        )
+
+    @classmethod
+    def execute(cls, boolean: bool) -> io.NodeOutput:
+        return io.NodeOutput(not boolean)
+
 class LogicExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
         return [
-            # SwitchNode,
+            SwitchNode,
+            CustomComboNode,
+            # SoftSwitchNode,
+            # ConvertStringToComboNode,
             # DCTestNode,
             # AutogrowNamesTestNode,
             # AutogrowPrefixTestNode,
+            # ComboOutputTestNode,
+            # InvertBooleanNode,
         ]
 
 async def comfy_entrypoint() -> LogicExtension:

comfy_extras/nodes_lt.py

@@ -81,6 +81,59 @@ class LTXVImgToVideo(io.ComfyNode):
     generate = execute  # TODO: remove
 
 
+class LTXVImgToVideoInplace(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVImgToVideoInplace",
+            category="conditioning/video_models",
+            inputs=[
+                io.Vae.Input("vae"),
+                io.Image.Input("image"),
+                io.Latent.Input("latent"),
+                io.Float.Input("strength", default=1.0, min=0.0, max=1.0),
+                io.Boolean.Input("bypass", default=False, tooltip="Bypass the conditioning.")
+            ],
+            outputs=[
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, vae, image, latent, strength, bypass=False) -> io.NodeOutput:
+        if bypass:
+            return (latent,)
+
+        samples = latent["samples"]
+        _, height_scale_factor, width_scale_factor = (
+            vae.downscale_index_formula
+        )
+
+        batch, _, latent_frames, latent_height, latent_width = samples.shape
+        width = latent_width * width_scale_factor
+        height = latent_height * height_scale_factor
+
+        if image.shape[1] != height or image.shape[2] != width:
+            pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+        else:
+            pixels = image
+        encode_pixels = pixels[:, :, :, :3]
+        t = vae.encode(encode_pixels)
+
+        samples[:, :, :t.shape[2]] = t
+
+        conditioning_latent_frames_mask = torch.ones(
+            (batch, 1, latent_frames, 1, 1),
+            dtype=torch.float32,
+            device=samples.device,
+        )
+        conditioning_latent_frames_mask[:, :, :t.shape[2]] = 1.0 - strength
+
+        return io.NodeOutput({"samples": samples, "noise_mask": conditioning_latent_frames_mask})
+
+    generate = execute  # TODO: remove
+
+
 def conditioning_get_any_value(conditioning, key, default=None):
     for t in conditioning:
         if key in t[1]:
@@ -106,12 +159,12 @@ def get_keyframe_idxs(cond):
     keyframe_idxs = conditioning_get_any_value(cond, "keyframe_idxs", None)
     if keyframe_idxs is None:
         return None, 0
-    num_keyframes = torch.unique(keyframe_idxs[:, 0]).shape[0]
+    # keyframe_idxs contains start/end positions (last dimension), checking for unqiue values only for start
+    num_keyframes = torch.unique(keyframe_idxs[:, 0, :, 0]).shape[0]
     return keyframe_idxs, num_keyframes
 
 class LTXVAddGuide(io.ComfyNode):
-    NUM_PREFIX_FRAMES = 2
+    PATCHIFIER = SymmetricPatchifier(1, start_end=True)
-    PATCHIFIER = SymmetricPatchifier(1)
 
     @classmethod
     def define_schema(cls):
@@ -182,26 +235,35 @@ class LTXVAddGuide(io.ComfyNode):
         return node_helpers.conditioning_set_values(cond, {"keyframe_idxs": keyframe_idxs})
 
     @classmethod
-    def append_keyframe(cls, positive, negative, frame_idx, latent_image, noise_mask, guiding_latent, strength, scale_factors):
+    def append_keyframe(cls, positive, negative, frame_idx, latent_image, noise_mask, guiding_latent, strength, scale_factors, guide_mask=None, in_channels=128):
-        _, latent_idx = cls.get_latent_index(
+        if latent_image.shape[1] != in_channels or guiding_latent.shape[1] != in_channels:
-            cond=positive,
+            raise ValueError("Adding guide to a combined AV latent is not supported.")
-            latent_length=latent_image.shape[2],
-            guide_length=guiding_latent.shape[2],
-            frame_idx=frame_idx,
-            scale_factors=scale_factors,
-        )
-        noise_mask[:, :, latent_idx:latent_idx + guiding_latent.shape[2]] = 1.0
 
         positive = cls.add_keyframe_index(positive, frame_idx, guiding_latent, scale_factors)
         negative = cls.add_keyframe_index(negative, frame_idx, guiding_latent, scale_factors)
 
-        mask = torch.full(
+        if guide_mask is not None:
-            (noise_mask.shape[0], 1, guiding_latent.shape[2], noise_mask.shape[3], noise_mask.shape[4]),
+            target_h = max(noise_mask.shape[3], guide_mask.shape[3])
-            1.0 - strength,
+            target_w = max(noise_mask.shape[4], guide_mask.shape[4])
-            dtype=noise_mask.dtype,
-            device=noise_mask.device,
-        )
 
+            if noise_mask.shape[3] == 1 or noise_mask.shape[4] == 1:
+                noise_mask = noise_mask.expand(-1, -1, -1, target_h, target_w)
+
+            if guide_mask.shape[3] == 1 or guide_mask.shape[4] == 1:
+                guide_mask = guide_mask.expand(-1, -1, -1, target_h, target_w)
+            mask = guide_mask - strength
+        else:
+            mask = torch.full(
+                (noise_mask.shape[0], 1, guiding_latent.shape[2], noise_mask.shape[3], noise_mask.shape[4]),
+                1.0 - strength,
+                dtype=noise_mask.dtype,
+                device=noise_mask.device,
+            )
+        # This solves audio video combined latent case where latent_image has audio latent concatenated
+        # in channel dimension with video latent. The solution is to pad guiding latent accordingly.
+        if latent_image.shape[1] > guiding_latent.shape[1]:
+            pad_len = latent_image.shape[1] - guiding_latent.shape[1]
+            guiding_latent = torch.nn.functional.pad(guiding_latent, pad=(0, 0, 0, 0, 0, 0, 0, pad_len), value=0)
         latent_image = torch.cat([latent_image, guiding_latent], dim=2)
         noise_mask = torch.cat([noise_mask, mask], dim=2)
         return positive, negative, latent_image, noise_mask
@@ -238,33 +300,17 @@ class LTXVAddGuide(io.ComfyNode):
         frame_idx, latent_idx = cls.get_latent_index(positive, latent_length, len(image), frame_idx, scale_factors)
         assert latent_idx + t.shape[2] <= latent_length, "Conditioning frames exceed the length of the latent sequence."
 
-        num_prefix_frames = min(cls.NUM_PREFIX_FRAMES, t.shape[2])
-
         positive, negative, latent_image, noise_mask = cls.append_keyframe(
             positive,
             negative,
             frame_idx,
             latent_image,
             noise_mask,
-            t[:, :, :num_prefix_frames],
+            t,
             strength,
             scale_factors,
         )
 
-        latent_idx += num_prefix_frames
-
-        t = t[:, :, num_prefix_frames:]
-        if t.shape[2] == 0:
-            return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask})
-
-        latent_image, noise_mask = cls.replace_latent_frames(
-            latent_image,
-            noise_mask,
-            t,
-            latent_idx,
-            strength,
-        )
-
         return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask})
 
     generate = execute  # TODO: remove
@@ -507,18 +553,90 @@ class LTXVPreprocess(io.ComfyNode):
 
     preprocess = execute  # TODO: remove
 
+
+import comfy.nested_tensor
+class LTXVConcatAVLatent(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVConcatAVLatent",
+            category="latent/video/ltxv",
+            inputs=[
+                io.Latent.Input("video_latent"),
+                io.Latent.Input("audio_latent"),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, video_latent, audio_latent) -> io.NodeOutput:
+        output = {}
+        output.update(video_latent)
+        output.update(audio_latent)
+        video_noise_mask = video_latent.get("noise_mask", None)
+        audio_noise_mask = audio_latent.get("noise_mask", None)
+
+        if video_noise_mask is not None or audio_noise_mask is not None:
+            if video_noise_mask is None:
+                video_noise_mask = torch.ones_like(video_latent["samples"])
+            if audio_noise_mask is None:
+                audio_noise_mask = torch.ones_like(audio_latent["samples"])
+            output["noise_mask"] = comfy.nested_tensor.NestedTensor((video_noise_mask, audio_noise_mask))
+
+        output["samples"] = comfy.nested_tensor.NestedTensor((video_latent["samples"], audio_latent["samples"]))
+
+        return io.NodeOutput(output)
+
+
+class LTXVSeparateAVLatent(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVSeparateAVLatent",
+            category="latent/video/ltxv",
+            description="LTXV Separate AV Latent",
+            inputs=[
+                io.Latent.Input("av_latent"),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="video_latent"),
+                io.Latent.Output(display_name="audio_latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, av_latent) -> io.NodeOutput:
+        latents = av_latent["samples"].unbind()
+        video_latent = av_latent.copy()
+        video_latent["samples"] = latents[0]
+        audio_latent = av_latent.copy()
+        audio_latent["samples"] = latents[1]
+        if "noise_mask" in av_latent:
+            masks = av_latent["noise_mask"]
+            if masks is not None:
+                masks = masks.unbind()
+                video_latent["noise_mask"] = masks[0]
+                audio_latent["noise_mask"] = masks[1]
+        return io.NodeOutput(video_latent, audio_latent)
+
+
 class LtxvExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
         return [
             EmptyLTXVLatentVideo,
             LTXVImgToVideo,
+            LTXVImgToVideoInplace,
             ModelSamplingLTXV,
             LTXVConditioning,
             LTXVScheduler,
             LTXVAddGuide,
             LTXVPreprocess,
             LTXVCropGuides,
+            LTXVConcatAVLatent,
+            LTXVSeparateAVLatent,
         ]
 
 

comfy_extras/nodes_lt_audio.py

@@ -0,0 +1,216 @@
+import folder_paths
+import comfy.utils
+import comfy.model_management
+import torch
+
+from comfy.ldm.lightricks.vae.audio_vae import AudioVAE
+from comfy_api.latest import ComfyExtension, io
+
+
+class LTXVAudioVAELoader(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LTXVAudioVAELoader",
+            display_name="LTXV Audio VAE Loader",
+            category="audio",
+            inputs=[
+                io.Combo.Input(
+                    "ckpt_name",
+                    options=folder_paths.get_filename_list("checkpoints"),
+                    tooltip="Audio VAE checkpoint to load.",
+                )
+            ],
+            outputs=[io.Vae.Output(display_name="Audio VAE")],
+        )
+
+    @classmethod
+    def execute(cls, ckpt_name: str) -> io.NodeOutput:
+        ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
+        sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True)
+        return io.NodeOutput(AudioVAE(sd, metadata))
+
+
+class LTXVAudioVAEEncode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LTXVAudioVAEEncode",
+            display_name="LTXV Audio VAE Encode",
+            category="audio",
+            inputs=[
+                io.Audio.Input("audio", tooltip="The audio to be encoded."),
+                io.Vae.Input(
+                    id="audio_vae",
+                    display_name="Audio VAE",
+                    tooltip="The Audio VAE model to use for encoding.",
+                ),
+            ],
+            outputs=[io.Latent.Output(display_name="Audio Latent")],
+        )
+
+    @classmethod
+    def execute(cls, audio, audio_vae: AudioVAE) -> io.NodeOutput:
+        audio_latents = audio_vae.encode(audio)
+        return io.NodeOutput(
+            {
+                "samples": audio_latents,
+                "sample_rate": int(audio_vae.sample_rate),
+                "type": "audio",
+            }
+        )
+
+
+class LTXVAudioVAEDecode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LTXVAudioVAEDecode",
+            display_name="LTXV Audio VAE Decode",
+            category="audio",
+            inputs=[
+                io.Latent.Input("samples", tooltip="The latent to be decoded."),
+                io.Vae.Input(
+                    id="audio_vae",
+                    display_name="Audio VAE",
+                    tooltip="The Audio VAE model used for decoding the latent.",
+                ),
+            ],
+            outputs=[io.Audio.Output(display_name="Audio")],
+        )
+
+    @classmethod
+    def execute(cls, samples, audio_vae: AudioVAE) -> io.NodeOutput:
+        audio_latent = samples["samples"]
+        if audio_latent.is_nested:
+            audio_latent = audio_latent.unbind()[-1]
+        audio = audio_vae.decode(audio_latent).to(audio_latent.device)
+        output_audio_sample_rate = audio_vae.output_sample_rate
+        return io.NodeOutput(
+            {
+                "waveform": audio,
+                "sample_rate": int(output_audio_sample_rate),
+            }
+        )
+
+
+class LTXVEmptyLatentAudio(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LTXVEmptyLatentAudio",
+            display_name="LTXV Empty Latent Audio",
+            category="latent/audio",
+            inputs=[
+                io.Int.Input(
+                    "frames_number",
+                    default=97,
+                    min=1,
+                    max=1000,
+                    step=1,
+                    display_mode=io.NumberDisplay.number,
+                    tooltip="Number of frames.",
+                ),
+                io.Int.Input(
+                    "frame_rate",
+                    default=25,
+                    min=1,
+                    max=1000,
+                    step=1,
+                    display_mode=io.NumberDisplay.number,
+                    tooltip="Number of frames per second.",
+                ),
+                io.Int.Input(
+                    "batch_size",
+                    default=1,
+                    min=1,
+                    max=4096,
+                    display_mode=io.NumberDisplay.number,
+                    tooltip="The number of latent audio samples in the batch.",
+                ),
+                io.Vae.Input(
+                    id="audio_vae",
+                    display_name="Audio VAE",
+                    tooltip="The Audio VAE model to get configuration from.",
+                ),
+            ],
+            outputs=[io.Latent.Output(display_name="Latent")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        frames_number: int,
+        frame_rate: int,
+        batch_size: int,
+        audio_vae: AudioVAE,
+    ) -> io.NodeOutput:
+        """Generate empty audio latents matching the reference pipeline structure."""
+
+        assert audio_vae is not None, "Audio VAE model is required"
+
+        z_channels = audio_vae.latent_channels
+        audio_freq = audio_vae.latent_frequency_bins
+        sampling_rate = int(audio_vae.sample_rate)
+
+        num_audio_latents = audio_vae.num_of_latents_from_frames(frames_number, frame_rate)
+
+        audio_latents = torch.zeros(
+            (batch_size, z_channels, num_audio_latents, audio_freq),
+            device=comfy.model_management.intermediate_device(),
+        )
+
+        return io.NodeOutput(
+            {
+                "samples": audio_latents,
+                "sample_rate": sampling_rate,
+                "type": "audio",
+            }
+        )
+
+
+class LTXAVTextEncoderLoader(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="LTXAVTextEncoderLoader",
+            display_name="LTXV Audio Text Encoder Loader",
+            category="advanced/loaders",
+            description="[Recipes]\n\nltxav: gemma 3 12B",
+            inputs=[
+                io.Combo.Input(
+                    "text_encoder",
+                    options=folder_paths.get_filename_list("text_encoders"),
+                ),
+                io.Combo.Input(
+                    "ckpt_name",
+                    options=folder_paths.get_filename_list("checkpoints"),
+                )
+            ],
+            outputs=[io.Clip.Output(display_name="Audio VAE")],
+        )
+
+    @classmethod
+    def execute(cls, text_encoder, ckpt_name, device="default"):
+        clip_type = comfy.sd.CLIPType.LTXV
+
+        clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", text_encoder)
+        clip_path2 = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
+
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
+        return io.NodeOutput(clip)
+
+
+class LTXVAudioExtension(ComfyExtension):
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            LTXVAudioVAELoader,
+            LTXVAudioVAEEncode,
+            LTXVAudioVAEDecode,
+            LTXVEmptyLatentAudio,
+            LTXAVTextEncoderLoader,
+        ]
+
+
+async def comfy_entrypoint() -> ComfyExtension:
+    return LTXVAudioExtension()

comfy_extras/nodes_lt_upsampler.py

@@ -0,0 +1,75 @@
+from comfy import model_management
+import math
+
+class LTXVLatentUpsampler:
+    """
+    Upsamples a video latent by a factor of 2.
+    """
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "samples": ("LATENT",),
+                "upscale_model": ("LATENT_UPSCALE_MODEL",),
+                "vae": ("VAE",),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "upsample_latent"
+    CATEGORY = "latent/video"
+    EXPERIMENTAL = True
+
+    def upsample_latent(
+        self,
+        samples: dict,
+        upscale_model,
+        vae,
+    ) -> tuple:
+        """
+        Upsample the input latent using the provided model.
+
+        Args:
+            samples (dict): Input latent samples
+            upscale_model (LatentUpsampler): Loaded upscale model
+            vae: VAE model for normalization
+            auto_tiling (bool): Whether to automatically tile the input for processing
+
+        Returns:
+            tuple: Tuple containing the upsampled latent
+        """
+        device = model_management.get_torch_device()
+        memory_required = model_management.module_size(upscale_model)
+
+        model_dtype = next(upscale_model.parameters()).dtype
+        latents = samples["samples"]
+        input_dtype = latents.dtype
+
+        memory_required += math.prod(latents.shape) * 3000.0  # TODO: more accurate
+        model_management.free_memory(memory_required, device)
+
+        try:
+            upscale_model.to(device)  # TODO: use the comfy model management system.
+
+            latents = latents.to(dtype=model_dtype, device=device)
+
+            """Upsample latents without tiling."""
+            latents = vae.first_stage_model.per_channel_statistics.un_normalize(latents)
+            upsampled_latents = upscale_model(latents)
+        finally:
+            upscale_model.cpu()
+
+        upsampled_latents = vae.first_stage_model.per_channel_statistics.normalize(
+            upsampled_latents
+        )
+        upsampled_latents = upsampled_latents.to(dtype=input_dtype, device=model_management.intermediate_device())
+        return_dict = samples.copy()
+        return_dict["samples"] = upsampled_latents
+        return_dict.pop("noise_mask", None)
+        return (return_dict,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "LTXVLatentUpsampler": LTXVLatentUpsampler,
+}

comfy_extras/nodes_mahiro.py

@@ -10,7 +10,7 @@ class Mahiro(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="Mahiro",
-            display_name="Mahiro is so cute that she deserves a better guidance function!! (。・ω・。)",
+            display_name="Mahiro CFG",
             category="_for_testing",
             description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.",
             inputs=[

comfy_extras/nodes_model_patch.py

@@ -348,7 +348,7 @@ class ZImageControlPatch:
             if self.mask is None:
                 mask_ = torch.zeros_like(inpaint_image_latent)[:, :1]
             else:
-                mask_ = comfy.utils.common_upscale(self.mask.view(self.mask.shape[0], -1, self.mask.shape[-2], self.mask.shape[-1]).mean(dim=1, keepdim=True), inpaint_image_latent.shape[-1], inpaint_image_latent.shape[-2], "nearest", "center")
+                mask_ = comfy.utils.common_upscale(self.mask.view(self.mask.shape[0], -1, self.mask.shape[-2], self.mask.shape[-1]).mean(dim=1, keepdim=True).to(device=inpaint_image_latent.device), inpaint_image_latent.shape[-1], inpaint_image_latent.shape[-2], "nearest", "center")
 
             if latent_image is None:
                 latent_image = comfy.latent_formats.Flux().process_in(self.vae.encode(torch.ones_like(inpaint_image) * 0.5))

comfy_extras/nodes_post_processing.py

@@ -4,11 +4,15 @@ import torch
 import torch.nn.functional as F
 from PIL import Image
 import math
+from enum import Enum
+from typing import TypedDict, Literal
 
 import comfy.utils
 import comfy.model_management
+from comfy_extras.nodes_latent import reshape_latent_to
 import node_helpers
 from comfy_api.latest import ComfyExtension, io
+from nodes import MAX_RESOLUTION
 
 class Blend(io.ComfyNode):
     @classmethod
@@ -241,6 +245,353 @@ class ImageScaleToTotalPixels(io.ComfyNode):
         s = s.movedim(1,-1)
         return io.NodeOutput(s)
 
+class ResizeType(str, Enum):
+    SCALE_BY = "scale by multiplier"
+    SCALE_DIMENSIONS = "scale dimensions"
+    SCALE_LONGER_DIMENSION = "scale longer dimension"
+    SCALE_SHORTER_DIMENSION = "scale shorter dimension"
+    SCALE_WIDTH = "scale width"
+    SCALE_HEIGHT = "scale height"
+    SCALE_TOTAL_PIXELS = "scale total pixels"
+    MATCH_SIZE = "match size"
+
+def is_image(input: torch.Tensor) -> bool:
+    # images have 4 dimensions: [batch, height, width, channels]
+    # masks have 3 dimensions: [batch, height, width]
+    return len(input.shape) == 4
+
+def init_image_mask_input(input: torch.Tensor, is_type_image: bool) -> torch.Tensor:
+    if is_type_image:
+        input = input.movedim(-1, 1)
+    else:
+        input = input.unsqueeze(1)
+    return input
+
+def finalize_image_mask_input(input: torch.Tensor, is_type_image: bool) -> torch.Tensor:
+    if is_type_image:
+        input = input.movedim(1, -1)
+    else:
+        input = input.squeeze(1)
+    return input
+
+def scale_by(input: torch.Tensor, multiplier: float, scale_method: str) -> torch.Tensor:
+    is_type_image = is_image(input)
+    input = init_image_mask_input(input, is_type_image)
+    width = round(input.shape[-1] * multiplier)
+    height = round(input.shape[-2] * multiplier)
+
+    input = comfy.utils.common_upscale(input, width, height, scale_method, "disabled")
+    input = finalize_image_mask_input(input, is_type_image)
+    return input
+
+def scale_dimensions(input: torch.Tensor, width: int, height: int, scale_method: str, crop: str="disabled") -> torch.Tensor:
+    if width == 0 and height == 0:
+        return input
+    is_type_image = is_image(input)
+    input = init_image_mask_input(input, is_type_image)
+
+    if width == 0:
+        width = max(1, round(input.shape[-1] * height / input.shape[-2]))
+    elif height == 0:
+        height = max(1, round(input.shape[-2] * width / input.shape[-1]))
+
+    input = comfy.utils.common_upscale(input, width, height, scale_method, crop)
+    input = finalize_image_mask_input(input, is_type_image)
+    return input
+
+def scale_longer_dimension(input: torch.Tensor, longer_size: int, scale_method: str) -> torch.Tensor:
+    is_type_image = is_image(input)
+    input = init_image_mask_input(input, is_type_image)
+    width = input.shape[-1]
+    height = input.shape[-2]
+
+    if height > width:
+        width = round((width / height) * longer_size)
+        height = longer_size
+    elif width > height:
+        height = round((height / width) * longer_size)
+        width = longer_size
+    else:
+        height = longer_size
+        width = longer_size
+
+    input = comfy.utils.common_upscale(input, width, height, scale_method, "disabled")
+    input = finalize_image_mask_input(input, is_type_image)
+    return input
+
+def scale_shorter_dimension(input: torch.Tensor, shorter_size: int, scale_method: str) -> torch.Tensor:
+    is_type_image = is_image(input)
+    input = init_image_mask_input(input, is_type_image)
+    width = input.shape[-1]
+    height = input.shape[-2]
+
+    if height < width:
+        width = round((width / height) * shorter_size)
+        height = shorter_size
+    elif width > height:
+        height = round((height / width) * shorter_size)
+        width = shorter_size
+    else:
+        height = shorter_size
+        width = shorter_size
+
+    input = comfy.utils.common_upscale(input, width, height, scale_method, "disabled")
+    input = finalize_image_mask_input(input, is_type_image)
+    return input
+
+def scale_total_pixels(input: torch.Tensor, megapixels: float, scale_method: str) -> torch.Tensor:
+    is_type_image = is_image(input)
+    input = init_image_mask_input(input, is_type_image)
+    total = int(megapixels * 1024 * 1024)
+
+    scale_by = math.sqrt(total / (input.shape[-1] * input.shape[-2]))
+    width = round(input.shape[-1] * scale_by)
+    height = round(input.shape[-2] * scale_by)
+
+    input = comfy.utils.common_upscale(input, width, height, scale_method, "disabled")
+    input = finalize_image_mask_input(input, is_type_image)
+    return input
+
+def scale_match_size(input: torch.Tensor, match: torch.Tensor, scale_method: str, crop: str) -> torch.Tensor:
+    is_type_image = is_image(input)
+    input = init_image_mask_input(input, is_type_image)
+    match = init_image_mask_input(match, is_image(match))
+
+    width = match.shape[-1]
+    height = match.shape[-2]
+    input = comfy.utils.common_upscale(input, width, height, scale_method, crop)
+    input = finalize_image_mask_input(input, is_type_image)
+    return input
+
+class ResizeImageMaskNode(io.ComfyNode):
+
+    scale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
+    crop_methods = ["disabled", "center"]
+
+    class ResizeTypedDict(TypedDict):
+        resize_type: ResizeType
+        scale_method: Literal["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
+        crop: Literal["disabled", "center"]
+        multiplier: float
+        width: int
+        height: int
+        longer_size: int
+        shorter_size: int
+        megapixels: float
+
+    @classmethod
+    def define_schema(cls):
+        template = io.MatchType.Template("input_type", [io.Image, io.Mask])
+        crop_combo = io.Combo.Input("crop", options=cls.crop_methods, default="center")
+        return io.Schema(
+            node_id="ResizeImageMaskNode",
+            display_name="Resize Image/Mask",
+            category="transform",
+            inputs=[
+                io.MatchType.Input("input", template=template),
+                io.DynamicCombo.Input("resize_type", options=[
+                    io.DynamicCombo.Option(ResizeType.SCALE_BY, [
+                        io.Float.Input("multiplier", default=1.00, min=0.01, max=8.0, step=0.01),
+                        ]),
+                    io.DynamicCombo.Option(ResizeType.SCALE_DIMENSIONS, [
+                        io.Int.Input("width", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        io.Int.Input("height", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        crop_combo,
+                        ]),
+                    io.DynamicCombo.Option(ResizeType.SCALE_LONGER_DIMENSION, [
+                        io.Int.Input("longer_size", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        ]),
+                    io.DynamicCombo.Option(ResizeType.SCALE_SHORTER_DIMENSION, [
+                        io.Int.Input("shorter_size", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        ]),
+                    io.DynamicCombo.Option(ResizeType.SCALE_WIDTH, [
+                        io.Int.Input("width", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        ]),
+                    io.DynamicCombo.Option(ResizeType.SCALE_HEIGHT, [
+                        io.Int.Input("height", default=512, min=0, max=MAX_RESOLUTION, step=1),
+                        ]),
+                    io.DynamicCombo.Option(ResizeType.SCALE_TOTAL_PIXELS, [
+                        io.Float.Input("megapixels", default=1.0, min=0.01, max=16.0, step=0.01),
+                        ]),
+                    io.DynamicCombo.Option(ResizeType.MATCH_SIZE, [
+                        io.MultiType.Input("match", [io.Image, io.Mask]),
+                        crop_combo,
+                        ]),
+                ]),
+                io.Combo.Input("scale_method", options=cls.scale_methods, default="area"),
+            ],
+            outputs=[io.MatchType.Output(template=template, display_name="resized")]
+        )
+
+    @classmethod
+    def execute(cls, input: io.Image.Type | io.Mask.Type, scale_method: io.Combo.Type, resize_type: ResizeTypedDict) -> io.NodeOutput:
+        selected_type = resize_type["resize_type"]
+        if selected_type == ResizeType.SCALE_BY:
+            return io.NodeOutput(scale_by(input, resize_type["multiplier"], scale_method))
+        elif selected_type == ResizeType.SCALE_DIMENSIONS:
+            return io.NodeOutput(scale_dimensions(input, resize_type["width"], resize_type["height"], scale_method, resize_type["crop"]))
+        elif selected_type == ResizeType.SCALE_LONGER_DIMENSION:
+            return io.NodeOutput(scale_longer_dimension(input, resize_type["longer_size"], scale_method))
+        elif selected_type == ResizeType.SCALE_SHORTER_DIMENSION:
+            return io.NodeOutput(scale_shorter_dimension(input, resize_type["shorter_size"], scale_method))
+        elif selected_type == ResizeType.SCALE_WIDTH:
+            return io.NodeOutput(scale_dimensions(input, resize_type["width"], 0, scale_method))
+        elif selected_type == ResizeType.SCALE_HEIGHT:
+            return io.NodeOutput(scale_dimensions(input, 0, resize_type["height"], scale_method))
+        elif selected_type == ResizeType.SCALE_TOTAL_PIXELS:
+            return io.NodeOutput(scale_total_pixels(input, resize_type["megapixels"], scale_method))
+        elif selected_type == ResizeType.MATCH_SIZE:
+            return io.NodeOutput(scale_match_size(input, resize_type["match"], scale_method, resize_type["crop"]))
+        raise ValueError(f"Unsupported resize type: {selected_type}")
+
+def batch_images(images: list[torch.Tensor]) -> torch.Tensor | None:
+    if len(images) == 0:
+        return None
+    # first, get the max channels count
+    max_channels = max(image.shape[-1] for image in images)
+    # then, pad all images to have the same channels count
+    padded_images: list[torch.Tensor] = []
+    for image in images:
+        if image.shape[-1] < max_channels:
+            padded_images.append(torch.nn.functional.pad(image, (0,1), mode='constant', value=1.0))
+        else:
+            padded_images.append(image)
+    # resize all images to be the same size as the first image
+    resized_images: list[torch.Tensor] = []
+    first_image_shape = padded_images[0].shape
+    for image in padded_images:
+        if image.shape[1:] != first_image_shape[1:]:
+            resized_images.append(comfy.utils.common_upscale(image.movedim(-1,1), first_image_shape[2], first_image_shape[1], "bilinear", "center").movedim(1,-1))
+        else:
+            resized_images.append(image)
+    # batch the images in the format [b, h, w, c]
+    return torch.cat(resized_images, dim=0)
+
+def batch_masks(masks: list[torch.Tensor]) -> torch.Tensor | None:
+    if len(masks) == 0:
+        return None
+    # resize all masks to be the same size as the first mask
+    resized_masks: list[torch.Tensor] = []
+    first_mask_shape = masks[0].shape
+    for mask in masks:
+        if mask.shape[1:] != first_mask_shape[1:]:
+            mask = init_image_mask_input(mask, is_type_image=False)
+            mask = comfy.utils.common_upscale(mask, first_mask_shape[2], first_mask_shape[1], "bilinear", "center")
+            resized_masks.append(finalize_image_mask_input(mask, is_type_image=False))
+        else:
+            resized_masks.append(mask)
+    # batch the masks in the format [b, h, w]
+    return torch.cat(resized_masks, dim=0)
+
+def batch_latents(latents: list[dict[str, torch.Tensor]]) -> dict[str, torch.Tensor] | None:
+    if len(latents) == 0:
+        return None
+    samples_out = latents[0].copy()
+    samples_out["batch_index"] = []
+    first_samples = latents[0]["samples"]
+    tensors: list[torch.Tensor] = []
+    for latent in latents:
+        # first, deal with latent tensors
+        tensors.append(reshape_latent_to(first_samples.shape, latent["samples"], repeat_batch=False))
+        # next, deal with batch_index
+        samples_out["batch_index"].extend(latent.get("batch_index", [x for x in range(0, latent["samples"].shape[0])]))
+    samples_out["samples"] = torch.cat(tensors, dim=0)
+    return samples_out
+
+class BatchImagesNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        autogrow_template = io.Autogrow.TemplatePrefix(io.Image.Input("image"), prefix="image", min=2, max=50)
+        return io.Schema(
+            node_id="BatchImagesNode",
+            display_name="Batch Images",
+            category="image",
+            inputs=[
+                io.Autogrow.Input("images", template=autogrow_template)
+            ],
+            outputs=[
+                io.Image.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, images: io.Autogrow.Type) -> io.NodeOutput:
+        return io.NodeOutput(batch_images(list(images.values())))
+
+class BatchMasksNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        autogrow_template = io.Autogrow.TemplatePrefix(io.Mask.Input("mask"), prefix="mask", min=2, max=50)
+        return io.Schema(
+            node_id="BatchMasksNode",
+            display_name="Batch Masks",
+            category="mask",
+            inputs=[
+                io.Autogrow.Input("masks", template=autogrow_template)
+            ],
+            outputs=[
+                io.Mask.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, masks: io.Autogrow.Type) -> io.NodeOutput:
+        return io.NodeOutput(batch_masks(list(masks.values())))
+
+class BatchLatentsNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        autogrow_template = io.Autogrow.TemplatePrefix(io.Latent.Input("latent"), prefix="latent", min=2, max=50)
+        return io.Schema(
+            node_id="BatchLatentsNode",
+            display_name="Batch Latents",
+            category="latent",
+            inputs=[
+                io.Autogrow.Input("latents", template=autogrow_template)
+            ],
+            outputs=[
+                io.Latent.Output()
+            ]
+        )
+
+    @classmethod
+    def execute(cls, latents: io.Autogrow.Type) -> io.NodeOutput:
+        return io.NodeOutput(batch_latents(list(latents.values())))
+
+class BatchImagesMasksLatentsNode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        matchtype_template = io.MatchType.Template("input", allowed_types=[io.Image, io.Mask, io.Latent])
+        autogrow_template = io.Autogrow.TemplatePrefix(
+                io.MatchType.Input("input", matchtype_template),
+                prefix="input", min=1, max=50)
+        return io.Schema(
+            node_id="BatchImagesMasksLatentsNode",
+            display_name="Batch Images/Masks/Latents",
+            category="util",
+            inputs=[
+                io.Autogrow.Input("inputs", template=autogrow_template)
+            ],
+            outputs=[
+                io.MatchType.Output(id=None, template=matchtype_template)
+            ]
+        )
+
+    @classmethod
+    def execute(cls, inputs: io.Autogrow.Type) -> io.NodeOutput:
+        batched = None
+        values = list(inputs.values())
+        # latents
+        if isinstance(values[0], dict):
+            batched = batch_latents(values)
+        # images
+        elif is_image(values[0]):
+            batched = batch_images(values)
+        # masks
+        else:
+            batched = batch_masks(values)
+        return io.NodeOutput(batched)
+
 class PostProcessingExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
@@ -250,6 +601,11 @@ class PostProcessingExtension(ComfyExtension):
             Quantize,
             Sharpen,
             ImageScaleToTotalPixels,
+            ResizeImageMaskNode,
+            BatchImagesNode,
+            BatchMasksNode,
+            BatchLatentsNode,
+            # BatchImagesMasksLatentsNode,
         ]
 
 async def comfy_entrypoint() -> PostProcessingExtension:

comfy_extras/nodes_primitive.py

@@ -66,7 +66,7 @@ class Float(io.ComfyNode):
             display_name="Float",
             category="utils/primitive",
             inputs=[
-                io.Float.Input("value", min=-sys.maxsize, max=sys.maxsize),
+                io.Float.Input("value", min=-sys.maxsize, max=sys.maxsize, step=0.1),
             ],
             outputs=[io.Float.Output()],
         )

comfy_extras/nodes_qwen.py

@@ -3,7 +3,9 @@ import comfy.utils
 import math
 from typing_extensions import override
 from comfy_api.latest import ComfyExtension, io
-
+import comfy.model_management
+import torch
+import nodes
 
 class TextEncodeQwenImageEdit(io.ComfyNode):
     @classmethod
@@ -104,12 +106,37 @@ class TextEncodeQwenImageEditPlus(io.ComfyNode):
         return io.NodeOutput(conditioning)
 
 
+class EmptyQwenImageLayeredLatentImage(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="EmptyQwenImageLayeredLatentImage",
+            display_name="Empty Qwen Image Layered Latent",
+            category="latent/qwen",
+            inputs=[
+                io.Int.Input("width", default=640, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=640, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("layers", default=3, min=0, max=nodes.MAX_RESOLUTION, step=1),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, width, height, layers, batch_size=1) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 16, layers + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        return io.NodeOutput({"samples": latent})
+
+
 class QwenExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
         return [
             TextEncodeQwenImageEdit,
             TextEncodeQwenImageEditPlus,
+            EmptyQwenImageLayeredLatentImage,
         ]
 
 

comfy_extras/nodes_upscale_model.py

@@ -78,18 +78,20 @@ class ImageUpscaleWithModel(io.ComfyNode):
         overlap = 32
 
         oom = True
-        while oom:
+        try:
-            try:
+            while oom:
-                steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap)
+                try:
-                pbar = comfy.utils.ProgressBar(steps)
+                    steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap)
-                s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
+                    pbar = comfy.utils.ProgressBar(steps)
-                oom = False
+                    s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
-            except model_management.OOM_EXCEPTION as e:
+                    oom = False
-                tile //= 2
+                except model_management.OOM_EXCEPTION as e:
-                if tile < 128:
+                    tile //= 2
-                    raise e
+                    if tile < 128:
+                        raise e
+        finally:
+            upscale_model.to("cpu")
 
-        upscale_model.to("cpu")
         s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0)
         return io.NodeOutput(s)
 

comfy_extras/nodes_wan.py

@@ -817,7 +817,7 @@ def get_sample_indices(original_fps,
     if required_duration > total_frames / original_fps:
         raise ValueError("required_duration must be less than video length")
 
-    if not fixed_start is None and fixed_start >= 0:
+    if fixed_start is not None and fixed_start >= 0:
         start_frame = fixed_start
     else:
         max_start = total_frames - required_origin_frames

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.5.1"
+__version__ = "0.7.0"

execution.py

@@ -79,7 +79,7 @@ class IsChangedCache:
         # Intentionally do not use cached outputs here. We only want constants in IS_CHANGED
         input_data_all, _, v3_data = get_input_data(node["inputs"], class_def, node_id, None)
         try:
-            is_changed = await _async_map_node_over_list(self.prompt_id, node_id, class_def, input_data_all, is_changed_name)
+            is_changed = await _async_map_node_over_list(self.prompt_id, node_id, class_def, input_data_all, is_changed_name, v3_data=v3_data)
             is_changed = await resolve_map_node_over_list_results(is_changed)
             node["is_changed"] = [None if isinstance(x, ExecutionBlocker) else x for x in is_changed]
         except Exception as e:
@@ -148,13 +148,12 @@ SENSITIVE_EXTRA_DATA_KEYS = ("auth_token_comfy_org", "api_key_comfy_org")
 def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=None, extra_data={}):
     is_v3 = issubclass(class_def, _ComfyNodeInternal)
     v3_data: io.V3Data = {}
+    hidden_inputs_v3 = {}
+    valid_inputs = class_def.INPUT_TYPES()
     if is_v3:
-        valid_inputs, schema, v3_data = class_def.INPUT_TYPES(include_hidden=False, return_schema=True, live_inputs=inputs)
+        valid_inputs, hidden, v3_data = _io.get_finalized_class_inputs(valid_inputs, inputs)
-    else:
-        valid_inputs = class_def.INPUT_TYPES()
     input_data_all = {}
     missing_keys = {}
-    hidden_inputs_v3 = {}
     for x in inputs:
         input_data = inputs[x]
         _, input_category, input_info = get_input_info(class_def, x, valid_inputs)
@@ -180,18 +179,18 @@ def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=
             input_data_all[x] = [input_data]
 
     if is_v3:
-        if schema.hidden:
+        if hidden is not None:
-            if io.Hidden.prompt in schema.hidden:
+            if io.Hidden.prompt.name in hidden:
                 hidden_inputs_v3[io.Hidden.prompt] = dynprompt.get_original_prompt() if dynprompt is not None else {}
-            if io.Hidden.dynprompt in schema.hidden:
+            if io.Hidden.dynprompt.name in hidden:
                 hidden_inputs_v3[io.Hidden.dynprompt] = dynprompt
-            if io.Hidden.extra_pnginfo in schema.hidden:
+            if io.Hidden.extra_pnginfo.name in hidden:
                 hidden_inputs_v3[io.Hidden.extra_pnginfo] = extra_data.get('extra_pnginfo', None)
-            if io.Hidden.unique_id in schema.hidden:
+            if io.Hidden.unique_id.name in hidden:
                 hidden_inputs_v3[io.Hidden.unique_id] = unique_id
-            if io.Hidden.auth_token_comfy_org in schema.hidden:
+            if io.Hidden.auth_token_comfy_org.name in hidden:
                 hidden_inputs_v3[io.Hidden.auth_token_comfy_org] = extra_data.get("auth_token_comfy_org", None)
-            if io.Hidden.api_key_comfy_org in schema.hidden:
+            if io.Hidden.api_key_comfy_org.name in hidden:
                 hidden_inputs_v3[io.Hidden.api_key_comfy_org] = extra_data.get("api_key_comfy_org", None)
     else:
         if "hidden" in valid_inputs:
@@ -258,7 +257,7 @@ async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, f
                 pre_execute_cb(index)
             # V3
             if isinstance(obj, _ComfyNodeInternal) or (is_class(obj) and issubclass(obj, _ComfyNodeInternal)):
-                # if is just a class, then assign no resources or state, just create clone
+                # if is just a class, then assign no state, just create clone
                 if is_class(obj):
                     type_obj = obj
                     obj.VALIDATE_CLASS()
@@ -481,7 +480,10 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
             else:
                 lazy_status_present = getattr(obj, "check_lazy_status", None) is not None
             if lazy_status_present:
-                required_inputs = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, "check_lazy_status", allow_interrupt=True, v3_data=v3_data)
+                # for check_lazy_status, the returned data should include the original key of the input
+                v3_data_lazy = v3_data.copy()
+                v3_data_lazy["create_dynamic_tuple"] = True
+                required_inputs = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, "check_lazy_status", allow_interrupt=True, v3_data=v3_data_lazy)
                 required_inputs = await resolve_map_node_over_list_results(required_inputs)
                 required_inputs = set(sum([r for r in required_inputs if isinstance(r,list)], []))
                 required_inputs = [x for x in required_inputs if isinstance(x,str) and (
@@ -599,6 +601,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
 
         if isinstance(ex, comfy.model_management.OOM_EXCEPTION):
             tips = "This error means you ran out of memory on your GPU.\n\nTIPS: If the workflow worked before you might have accidentally set the batch_size to a large number."
+            logging.info("Memory summary: {}".format(comfy.model_management.debug_memory_summary()))
             logging.error("Got an OOM, unloading all loaded models.")
             comfy.model_management.unload_all_models()
 
@@ -756,10 +759,13 @@ async def validate_inputs(prompt_id, prompt, item, validated):
     errors = []
     valid = True
 
+    v3_data = None
     validate_function_inputs = []
     validate_has_kwargs = False
     if issubclass(obj_class, _ComfyNodeInternal):
-        class_inputs, _, _ = obj_class.INPUT_TYPES(include_hidden=False, return_schema=True, live_inputs=inputs)
+        obj_class: _io._ComfyNodeBaseInternal
+        class_inputs = obj_class.INPUT_TYPES()
+        class_inputs, _, v3_data = _io.get_finalized_class_inputs(class_inputs, inputs)
         validate_function_name = "validate_inputs"
         validate_function = first_real_override(obj_class, validate_function_name)
     else:
@@ -779,10 +785,11 @@ async def validate_inputs(prompt_id, prompt, item, validated):
         assert extra_info is not None
         if x not in inputs:
             if input_category == "required":
+                details = f"{x}" if not v3_data else x.split(".")[-1]
                 error = {
                     "type": "required_input_missing",
                     "message": "Required input is missing",
-                    "details": f"{x}",
+                    "details": details,
                     "extra_info": {
                         "input_name": x
                     }
@@ -916,8 +923,11 @@ async def validate_inputs(prompt_id, prompt, item, validated):
                     errors.append(error)
                     continue
 
-                if isinstance(input_type, list):
+                if isinstance(input_type, list) or input_type == io.Combo.io_type:
-                    combo_options = input_type
+                    if input_type == io.Combo.io_type:
+                        combo_options = extra_info.get("options", [])
+                    else:
+                        combo_options = input_type
                     if val not in combo_options:
                         input_config = info
                         list_info = ""

manager_requirements.txt

@@ -1 +1 @@
-comfyui_manager==4.0.3b7
+comfyui_manager==4.0.4

nodes.py

@@ -295,7 +295,11 @@ class VAEDecode:
     DESCRIPTION = "Decodes latent images back into pixel space images."
 
     def decode(self, vae, samples):
-        images = vae.decode(samples["samples"])
+        latent = samples["samples"]
+        if latent.is_nested:
+            latent = latent.unbind()[0]
+
+        images = vae.decode(latent)
         if len(images.shape) == 5: #Combine batches
             images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
         return (images, )
@@ -970,7 +974,7 @@ class DualCLIPLoader:
     def INPUT_TYPES(s):
         return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ),
                               "clip_name2": (folder_paths.get_filename_list("text_encoders"), ),
-                              "type": (["sdxl", "sd3", "flux", "hunyuan_video", "hidream", "hunyuan_image", "hunyuan_video_15", "kandinsky5", "kandinsky5_image"], ),
+                              "type": (["sdxl", "sd3", "flux", "hunyuan_video", "hidream", "hunyuan_image", "hunyuan_video_15", "kandinsky5", "kandinsky5_image", "ltxv", "newbie"], ),
                               },
                 "optional": {
                               "device": (["default", "cpu"], {"advanced": True}),
@@ -980,7 +984,7 @@ class DualCLIPLoader:
 
     CATEGORY = "advanced/loaders"
 
-    DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama\nhunyuan_image: qwen2.5vl 7b and byt5 small"
+    DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama\nhunyuan_image: qwen2.5vl 7b and byt5 small\nnewbie: gemma-3-4b-it, jina clip v2"
 
     def load_clip(self, clip_name1, clip_name2, type, device="default"):
         clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
@@ -1663,8 +1667,6 @@ class LoadImage:
         output_masks = []
         w, h = None, None
 
-        excluded_formats = ['MPO']
-
         for i in ImageSequence.Iterator(img):
             i = node_helpers.pillow(ImageOps.exif_transpose, i)
 
@@ -1692,7 +1694,10 @@ class LoadImage:
             output_images.append(image)
             output_masks.append(mask.unsqueeze(0))
 
-        if len(output_images) > 1 and img.format not in excluded_formats:
+            if img.format == "MPO":
+                break  # ignore all frames except the first one for MPO format
+
+        if len(output_images) > 1:
             output_image = torch.cat(output_images, dim=0)
             output_mask = torch.cat(output_masks, dim=0)
         else:
@@ -1863,6 +1868,7 @@ class ImageBatch:
     FUNCTION = "batch"
 
     CATEGORY = "image"
+    DEPRECATED = True
 
     def batch(self, image1, image2):
         if image1.shape[-1] != image2.shape[-1]:
@@ -2241,8 +2247,10 @@ async def init_external_custom_nodes():
 
         for possible_module in possible_modules:
             module_path = os.path.join(custom_node_path, possible_module)
-            if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue
+            if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py":
-            if module_path.endswith(".disabled"): continue
+                continue
+            if module_path.endswith(".disabled"):
+                continue
             if args.disable_all_custom_nodes and possible_module not in args.whitelist_custom_nodes:
                 logging.info(f"Skipping {possible_module} due to disable_all_custom_nodes and whitelist_custom_nodes")
                 continue
@@ -2327,6 +2335,8 @@ async def init_builtin_extra_nodes():
         "nodes_mochi.py",
         "nodes_slg.py",
         "nodes_mahiro.py",
+        "nodes_lt_upsampler.py",
+        "nodes_lt_audio.py",
         "nodes_lt.py",
         "nodes_hooks.py",
         "nodes_load_3d.py",

pyproject.toml

@@ -1,9 +1,9 @@
 [project]
 name = "ComfyUI"
-version = "0.5.1"
+version = "0.7.0"
 readme = "README.md"
 license = { file = "LICENSE" }
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 
 [project.urls]
 homepage = "https://www.comfy.org/"
@@ -15,12 +15,16 @@ lint.select = [
   "N805", # invalid-first-argument-name-for-method
   "S307", # suspicious-eval-usage
   "S102", # exec
+  "E",
   "T",    # print-usage
   "W",
   # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names.
   # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f
   "F",
 ]
+
+lint.ignore = ["E501", "E722", "E731", "E712", "E402", "E741"]
+
 exclude = ["*.ipynb", "**/generated/*.pyi"]
 
 [tool.pylint]

requirements.txt

@@ -1,5 +1,5 @@
-comfyui-frontend-package==1.34.9
+comfyui-frontend-package==1.35.9
-comfyui-workflow-templates==0.7.60
+comfyui-workflow-templates==0.7.65
 comfyui-embedded-docs==0.3.1
 torch
 torchsde

server.py

@@ -324,7 +324,7 @@ class PromptServer():
         @routes.get("/models/{folder}")
         async def get_models(request):
             folder = request.match_info.get("folder", None)
-            if not folder in folder_paths.folder_names_and_paths:
+            if folder not in folder_paths.folder_names_and_paths:
                 return web.Response(status=404)
             files = folder_paths.get_filename_list(folder)
             return web.json_response(files)
@@ -579,7 +579,7 @@ class PromptServer():
             folder_name = request.match_info.get("folder_name", None)
             if folder_name is None:
                 return web.Response(status=404)
-            if not "filename" in request.rel_url.query:
+            if "filename" not in request.rel_url.query:
                 return web.Response(status=404)
 
             filename = request.rel_url.query["filename"]
@@ -593,7 +593,7 @@ class PromptServer():
             if out is None:
                 return web.Response(status=404)
             dt = json.loads(out)
-            if not "__metadata__" in dt:
+            if "__metadata__" not in dt:
                 return web.Response(status=404)
             return web.json_response(dt["__metadata__"])
 

tests-unit/comfy_extras_test/image_stitch_test.py

@@ -25,7 +25,7 @@ class TestImageStitch:
 
         result = node.stitch(image1, "right", True, 0, "white", image2=None)
 
-        assert len(result) == 1
+        assert len(result.result) == 1
         assert torch.equal(result[0], image1)
 
     def test_basic_horizontal_stitch_right(self):