Merge branch 'Comfy-Org:master' into enable-triton-comfy-kitchen

comfy/latent_formats.py

@@ -776,3 +776,10 @@ class ChromaRadiance(LatentFormat):
 
     def process_out(self, latent):
         return latent
+
+
+class ZImagePixelSpace(ChromaRadiance):
+    """Pixel-space latent format for ZImage DCT variant.
+    No VAE encoding/decoding — the model operates directly on RGB pixels.
+    """
+    pass

comfy/ldm/lightricks/av_model.py

@@ -2,11 +2,16 @@ from typing import Tuple
 import torch
 import torch.nn as nn
 from comfy.ldm.lightricks.model import (
+    ADALN_BASE_PARAMS_COUNT,
+    ADALN_CROSS_ATTN_PARAMS_COUNT,
     CrossAttention,
     FeedForward,
     AdaLayerNormSingle,
     PixArtAlphaTextProjection,
+    NormSingleLinearTextProjection,
     LTXVModel,
+    apply_cross_attention_adaln,
+    compute_prompt_timestep,
 )
 from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier
 from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
@@ -87,6 +92,8 @@ class BasicAVTransformerBlock(nn.Module):
         v_context_dim=None,
         a_context_dim=None,
         attn_precision=None,
+        apply_gated_attention=False,
+        cross_attention_adaln=False,
         dtype=None,
         device=None,
         operations=None,
@@ -94,6 +101,7 @@ class BasicAVTransformerBlock(nn.Module):
         super().__init__()
 
         self.attn_precision = attn_precision
+        self.cross_attention_adaln = cross_attention_adaln
 
         self.attn1 = CrossAttention(
             query_dim=v_dim,
@@ -101,6 +109,7 @@ class BasicAVTransformerBlock(nn.Module):
             dim_head=vd_head,
             context_dim=None,
             attn_precision=self.attn_precision,
+            apply_gated_attention=apply_gated_attention,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -111,6 +120,7 @@ class BasicAVTransformerBlock(nn.Module):
             dim_head=ad_head,
             context_dim=None,
             attn_precision=self.attn_precision,
+            apply_gated_attention=apply_gated_attention,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -122,6 +132,7 @@ class BasicAVTransformerBlock(nn.Module):
             heads=v_heads,
             dim_head=vd_head,
             attn_precision=self.attn_precision,
+            apply_gated_attention=apply_gated_attention,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -132,6 +143,7 @@ class BasicAVTransformerBlock(nn.Module):
             heads=a_heads,
             dim_head=ad_head,
             attn_precision=self.attn_precision,
+            apply_gated_attention=apply_gated_attention,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -144,6 +156,7 @@ class BasicAVTransformerBlock(nn.Module):
             heads=a_heads,
             dim_head=ad_head,
             attn_precision=self.attn_precision,
+            apply_gated_attention=apply_gated_attention,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -156,6 +169,7 @@ class BasicAVTransformerBlock(nn.Module):
             heads=a_heads,
             dim_head=ad_head,
             attn_precision=self.attn_precision,
+            apply_gated_attention=apply_gated_attention,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -168,11 +182,16 @@ class BasicAVTransformerBlock(nn.Module):
             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))
+        num_ada_params = ADALN_CROSS_ATTN_PARAMS_COUNT if cross_attention_adaln else ADALN_BASE_PARAMS_COUNT
+        self.scale_shift_table = nn.Parameter(torch.empty(num_ada_params, v_dim, device=device, dtype=dtype))
         self.audio_scale_shift_table = nn.Parameter(
-            torch.empty(6, a_dim, device=device, dtype=dtype)
+            torch.empty(num_ada_params, a_dim, device=device, dtype=dtype)
         )
 
+        if cross_attention_adaln:
+            self.prompt_scale_shift_table = nn.Parameter(torch.empty(2, v_dim, device=device, dtype=dtype))
+            self.audio_prompt_scale_shift_table = nn.Parameter(torch.empty(2, a_dim, device=device, dtype=dtype))
+
         self.scale_shift_table_a2v_ca_audio = nn.Parameter(
             torch.empty(5, a_dim, device=device, dtype=dtype)
         )
@@ -215,10 +234,30 @@ class BasicAVTransformerBlock(nn.Module):
 
         return (*scale_shift_ada_values, *gate_ada_values)
 
+    def _apply_text_cross_attention(
+        self, x, context, attn, scale_shift_table, prompt_scale_shift_table,
+        timestep, prompt_timestep, attention_mask, transformer_options,
+    ):
+        """Apply text cross-attention, with optional ADaLN modulation."""
+        if self.cross_attention_adaln:
+            shift_q, scale_q, gate = self.get_ada_values(
+                scale_shift_table, x.shape[0], timestep, slice(6, 9)
+            )
+            return apply_cross_attention_adaln(
+                x, context, attn, shift_q, scale_q, gate,
+                prompt_scale_shift_table, prompt_timestep,
+                attention_mask, transformer_options,
+            )
+        return attn(
+            comfy.ldm.common_dit.rms_norm(x), context=context,
+            mask=attention_mask, transformer_options=transformer_options,
+        )
+
     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, self_attention_mask=None,
+        v_prompt_timestep=None, a_prompt_timestep=None,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         run_vx = transformer_options.get("run_vx", True)
         run_ax = transformer_options.get("run_ax", True)
@@ -240,7 +279,11 @@ class BasicAVTransformerBlock(nn.Module):
             vgate_msa = self.get_ada_values(self.scale_shift_table, vx.shape[0], v_timestep, slice(2, 3))[0]
             vx.addcmul_(attn1_out, vgate_msa)
             del vgate_msa, attn1_out
-            vx.add_(self.attn2(comfy.ldm.common_dit.rms_norm(vx), context=v_context, mask=attention_mask, transformer_options=transformer_options))
+            vx.add_(self._apply_text_cross_attention(
+                vx, v_context, self.attn2, self.scale_shift_table,
+                getattr(self, 'prompt_scale_shift_table', None),
+                v_timestep, v_prompt_timestep, attention_mask, transformer_options,)
+            )
 
         # audio
         if run_ax:
@@ -254,7 +297,11 @@ class BasicAVTransformerBlock(nn.Module):
             agate_msa = self.get_ada_values(self.audio_scale_shift_table, ax.shape[0], a_timestep, slice(2, 3))[0]
             ax.addcmul_(attn1_out, agate_msa)
             del agate_msa, attn1_out
-            ax.add_(self.audio_attn2(comfy.ldm.common_dit.rms_norm(ax), context=a_context, mask=attention_mask, transformer_options=transformer_options))
+            ax.add_(self._apply_text_cross_attention(
+                ax, a_context, self.audio_attn2, self.audio_scale_shift_table,
+                getattr(self, 'audio_prompt_scale_shift_table', None),
+                a_timestep, a_prompt_timestep, attention_mask, transformer_options,)
+            )
 
         # video - audio cross attention.
         if run_a2v or run_v2a:
@@ -351,6 +398,9 @@ class LTXAVModel(LTXVModel):
         use_middle_indices_grid=False,
         timestep_scale_multiplier=1000.0,
         av_ca_timestep_scale_multiplier=1.0,
+        apply_gated_attention=False,
+        caption_proj_before_connector=False,
+        cross_attention_adaln=False,
         dtype=None,
         device=None,
         operations=None,
@@ -362,6 +412,7 @@ class LTXAVModel(LTXVModel):
         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
+        self.apply_gated_attention = apply_gated_attention
 
         # Calculate audio dimensions
         self.audio_inner_dim = audio_num_attention_heads * audio_attention_head_dim
@@ -386,6 +437,8 @@ class LTXAVModel(LTXVModel):
             vae_scale_factors=vae_scale_factors,
             use_middle_indices_grid=use_middle_indices_grid,
             timestep_scale_multiplier=timestep_scale_multiplier,
+            caption_proj_before_connector=caption_proj_before_connector,
+            cross_attention_adaln=cross_attention_adaln,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -400,14 +453,28 @@ class LTXAVModel(LTXVModel):
         )
 
         # Audio-specific AdaLN
+        audio_embedding_coefficient = ADALN_CROSS_ATTN_PARAMS_COUNT if self.cross_attention_adaln else ADALN_BASE_PARAMS_COUNT
         self.audio_adaln_single = AdaLayerNormSingle(
             self.audio_inner_dim,
+            embedding_coefficient=audio_embedding_coefficient,
             use_additional_conditions=False,
             dtype=dtype,
             device=device,
             operations=self.operations,
         )
 
+        if self.cross_attention_adaln:
+            self.audio_prompt_adaln_single = AdaLayerNormSingle(
+                self.audio_inner_dim,
+                embedding_coefficient=2,
+                use_additional_conditions=False,
+                dtype=dtype,
+                device=device,
+                operations=self.operations,
+            )
+        else:
+            self.audio_prompt_adaln_single = None
+
         num_scale_shift_values = 4
         self.av_ca_video_scale_shift_adaln_single = AdaLayerNormSingle(
             self.inner_dim,
@@ -443,35 +510,73 @@ class LTXAVModel(LTXVModel):
         )
 
         # 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,
-        )
+        if self.caption_proj_before_connector:
+            if self.caption_projection_first_linear:
+                self.audio_caption_projection = NormSingleLinearTextProjection(
+                    in_features=self.caption_channels,
+                    hidden_size=self.audio_inner_dim,
+                    dtype=dtype,
+                    device=device,
+                    operations=self.operations,
+                )
+            else:
+                self.audio_caption_projection = lambda a: a
+        else:
+            self.audio_caption_projection = PixArtAlphaTextProjection(
+                in_features=self.caption_channels,
+                hidden_size=self.audio_inner_dim,
+                dtype=dtype,
+                device=device,
+                operations=self.operations,
+            )
+
+        connector_split_rope = kwargs.get("rope_type", "split") == "split"
+        connector_gated_attention = kwargs.get("connector_apply_gated_attention", False)
+        attention_head_dim = kwargs.get("connector_attention_head_dim", 128)
+        num_attention_heads = kwargs.get("connector_num_attention_heads", 30)
+        num_layers = kwargs.get("connector_num_layers", 2)
 
         self.audio_embeddings_connector = Embeddings1DConnector(
-            split_rope=True,
+            attention_head_dim=kwargs.get("audio_connector_attention_head_dim", attention_head_dim),
+            num_attention_heads=kwargs.get("audio_connector_num_attention_heads", num_attention_heads),
+            num_layers=num_layers,
+            split_rope=connector_split_rope,
             double_precision_rope=True,
+            apply_gated_attention=connector_gated_attention,
             dtype=dtype,
             device=device,
             operations=self.operations,
         )
 
         self.video_embeddings_connector = Embeddings1DConnector(
-            split_rope=True,
+            attention_head_dim=attention_head_dim,
+            num_attention_heads=num_attention_heads,
+            num_layers=num_layers,
+            split_rope=connector_split_rope,
             double_precision_rope=True,
+            apply_gated_attention=connector_gated_attention,
             dtype=dtype,
             device=device,
             operations=self.operations,
         )
 
-    def preprocess_text_embeds(self, context):
-        if context.shape[-1] == self.caption_channels * 2:
-            return context
-        out_vid = self.video_embeddings_connector(context)[0]
-        out_audio = self.audio_embeddings_connector(context)[0]
+    def preprocess_text_embeds(self, context, unprocessed=False):
+        # LTXv2 fully processed context has dimension of self.caption_channels * 2
+        # LTXv2.3 fully processed context has dimension of self.cross_attention_dim + self.audio_cross_attention_dim
+        if not unprocessed:
+            if context.shape[-1] in (self.cross_attention_dim + self.audio_cross_attention_dim, self.caption_channels * 2):
+                return context
+        if context.shape[-1] == self.cross_attention_dim + self.audio_cross_attention_dim:
+            context_vid = context[:, :, :self.cross_attention_dim]
+            context_audio = context[:, :, self.cross_attention_dim:]
+        else:
+            context_vid = context
+            context_audio = context
+        if self.caption_proj_before_connector:
+            context_vid = self.caption_projection(context_vid)
+            context_audio = self.audio_caption_projection(context_audio)
+        out_vid = self.video_embeddings_connector(context_vid)[0]
+        out_audio = self.audio_embeddings_connector(context_audio)[0]
         return torch.concat((out_vid, out_audio), dim=-1)
 
     def _init_transformer_blocks(self, device, dtype, **kwargs):
@@ -487,6 +592,8 @@ class LTXAVModel(LTXVModel):
                     ad_head=self.audio_attention_head_dim,
                     v_context_dim=self.cross_attention_dim,
                     a_context_dim=self.audio_cross_attention_dim,
+                    apply_gated_attention=self.apply_gated_attention,
+                    cross_attention_adaln=self.cross_attention_adaln,
                     dtype=dtype,
                     device=device,
                     operations=self.operations,
@@ -608,6 +715,10 @@ class LTXAVModel(LTXVModel):
         v_timestep = CompressedTimestep(v_timestep.view(batch_size, -1, v_timestep.shape[-1]), v_patches_per_frame)
         v_embedded_timestep = CompressedTimestep(v_embedded_timestep.view(batch_size, -1, v_embedded_timestep.shape[-1]), v_patches_per_frame)
 
+        v_prompt_timestep = compute_prompt_timestep(
+            self.prompt_adaln_single, timestep_scaled, batch_size, hidden_dtype
+        )
+
         # Prepare audio timestep
         a_timestep = kwargs.get("a_timestep")
         if a_timestep is not None:
@@ -618,25 +729,25 @@ class LTXAVModel(LTXVModel):
 
             # Cross-attention timesteps - compress these too
             av_ca_audio_scale_shift_timestep, _ = self.av_ca_audio_scale_shift_adaln_single(
-                a_timestep_flat,
+                timestep.max().expand_as(a_timestep_flat),
                 {"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_flat,
+                a_timestep.max().expand_as(timestep_flat),
                 {"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_flat * av_ca_factor,
+                a_timestep.max().expand_as(timestep_flat) * 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_flat * av_ca_factor,
+                timestep.max().expand_as(a_timestep_flat) * av_ca_factor,
                 {"resolution": None, "aspect_ratio": None},
                 batch_size=batch_size,
                 hidden_dtype=hidden_dtype,
@@ -660,29 +771,40 @@ class LTXAVModel(LTXVModel):
             # Audio timesteps
             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])
+
+            a_prompt_timestep = compute_prompt_timestep(
+                self.audio_prompt_adaln_single, a_timestep_scaled, batch_size, hidden_dtype
+            )
         else:
             a_timestep = timestep_scaled
             a_embedded_timestep = kwargs.get("embedded_timestep")
             cross_av_timestep_ss = []
+            a_prompt_timestep = None
 
-        return [v_timestep, a_timestep, cross_av_timestep_ss], [
+        return [v_timestep, a_timestep, cross_av_timestep_ss, v_prompt_timestep, a_prompt_timestep], [
             v_embedded_timestep,
             a_embedded_timestep,
-        ]
+        ], None
 
     def _prepare_context(self, context, batch_size, x, attention_mask=None):
         vx = x[0]
         ax = x[1]
+        video_dim = vx.shape[-1]
+        audio_dim = ax.shape[-1]
+
+        v_context_dim = self.caption_channels if self.caption_proj_before_connector is False else video_dim
+        a_context_dim = self.caption_channels if self.caption_proj_before_connector is False else audio_dim
+
         v_context, a_context = torch.split(
-            context, int(context.shape[-1] / 2), len(context.shape) - 1
+            context, [v_context_dim, a_context_dim], 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:
+        if self.caption_proj_before_connector is False:
             a_context = self.audio_caption_projection(a_context)
-            a_context = a_context.view(batch_size, -1, ax.shape[-1])
+        a_context = a_context.view(batch_size, -1, audio_dim)
 
         return [v_context, a_context], attention_mask
 
@@ -744,6 +866,9 @@ class LTXAVModel(LTXVModel):
             av_ca_v2a_gate_noise_timestep,
         ) = timestep[2]
 
+        v_prompt_timestep = timestep[3]
+        a_prompt_timestep = timestep[4]
+
         """Process transformer blocks for LTXAV."""
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
@@ -771,6 +896,8 @@ class LTXAVModel(LTXVModel):
                         a_cross_gate_timestep=args["a_cross_gate_timestep"],
                         transformer_options=args["transformer_options"],
                         self_attention_mask=args.get("self_attention_mask"),
+                        v_prompt_timestep=args.get("v_prompt_timestep"),
+                        a_prompt_timestep=args.get("a_prompt_timestep"),
                     )
                     return out
 
@@ -792,6 +919,8 @@ class LTXAVModel(LTXVModel):
                         "a_cross_gate_timestep": av_ca_v2a_gate_noise_timestep,
                         "transformer_options": transformer_options,
                         "self_attention_mask": self_attention_mask,
+                        "v_prompt_timestep": v_prompt_timestep,
+                        "a_prompt_timestep": a_prompt_timestep,
                     },
                     {"original_block": block_wrap},
                 )
@@ -814,6 +943,8 @@ class LTXAVModel(LTXVModel):
                     a_cross_gate_timestep=av_ca_v2a_gate_noise_timestep,
                     transformer_options=transformer_options,
                     self_attention_mask=self_attention_mask,
+                    v_prompt_timestep=v_prompt_timestep,
+                    a_prompt_timestep=a_prompt_timestep,
                 )
 
         return [vx, ax]

comfy/ldm/lightricks/embeddings_connector.py

@@ -50,6 +50,7 @@ class BasicTransformerBlock1D(nn.Module):
         d_head,
         context_dim=None,
         attn_precision=None,
+        apply_gated_attention=False,
         dtype=None,
         device=None,
         operations=None,
@@ -63,6 +64,7 @@ class BasicTransformerBlock1D(nn.Module):
             heads=n_heads,
             dim_head=d_head,
             context_dim=None,
+            apply_gated_attention=apply_gated_attention,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -121,6 +123,7 @@ class Embeddings1DConnector(nn.Module):
         positional_embedding_max_pos=[4096],
         causal_temporal_positioning=False,
         num_learnable_registers: Optional[int] = 128,
+        apply_gated_attention=False,
         dtype=None,
         device=None,
         operations=None,
@@ -145,6 +148,7 @@ class Embeddings1DConnector(nn.Module):
                     num_attention_heads,
                     attention_head_dim,
                     context_dim=cross_attention_dim,
+                    apply_gated_attention=apply_gated_attention,
                     dtype=dtype,
                     device=device,
                     operations=operations,

comfy/ldm/lightricks/model.py

@@ -275,6 +275,30 @@ class PixArtAlphaTextProjection(nn.Module):
         return hidden_states
 
 
+class NormSingleLinearTextProjection(nn.Module):
+    """Text projection for 20B models - single linear with RMSNorm (no activation)."""
+
+    def __init__(
+        self, in_features, hidden_size, dtype=None, device=None, operations=None
+    ):
+        super().__init__()
+        if operations is None:
+            operations = comfy.ops.disable_weight_init
+        self.in_norm = operations.RMSNorm(
+            in_features, eps=1e-6, elementwise_affine=False
+        )
+        self.linear_1 = operations.Linear(
+            in_features, hidden_size, bias=True, dtype=dtype, device=device
+        )
+        self.hidden_size = hidden_size
+        self.in_features = in_features
+
+    def forward(self, caption):
+        caption = self.in_norm(caption)
+        caption = caption * (self.hidden_size / self.in_features) ** 0.5
+        return self.linear_1(caption)
+
+
 class GELU_approx(nn.Module):
     def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=None):
         super().__init__()
@@ -343,6 +367,7 @@ class CrossAttention(nn.Module):
         dim_head=64,
         dropout=0.0,
         attn_precision=None,
+        apply_gated_attention=False,
         dtype=None,
         device=None,
         operations=None,
@@ -362,6 +387,12 @@ 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)
 
+        # Optional per-head gating
+        if apply_gated_attention:
+            self.to_gate_logits = operations.Linear(query_dim, heads, bias=True, dtype=dtype, device=device)
+        else:
+            self.to_gate_logits = None
+
         self.to_out = nn.Sequential(
             operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)
         )
@@ -383,16 +414,30 @@ class CrossAttention(nn.Module):
             out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
         else:
             out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
+
+        # Apply per-head gating if enabled
+        if self.to_gate_logits is not None:
+            gate_logits = self.to_gate_logits(x)  # (B, T, H)
+            b, t, _ = out.shape
+            out = out.view(b, t, self.heads, self.dim_head)
+            gates = 2.0 * torch.sigmoid(gate_logits)  # zero-init -> identity
+            out = out * gates.unsqueeze(-1)
+            out = out.view(b, t, self.heads * self.dim_head)
+
         return self.to_out(out)
 
+# 6 base ADaLN params (shift/scale/gate for MSA + MLP), +3 for cross-attention Q (shift/scale/gate)
+ADALN_BASE_PARAMS_COUNT = 6
+ADALN_CROSS_ATTN_PARAMS_COUNT = 9
 
 class BasicTransformerBlock(nn.Module):
     def __init__(
-        self, dim, n_heads, d_head, context_dim=None, attn_precision=None, dtype=None, device=None, operations=None
+        self, dim, n_heads, d_head, context_dim=None, attn_precision=None, cross_attention_adaln=False, dtype=None, device=None, operations=None
     ):
         super().__init__()
 
         self.attn_precision = attn_precision
+        self.cross_attention_adaln = cross_attention_adaln
         self.attn1 = CrossAttention(
             query_dim=dim,
             heads=n_heads,
@@ -416,18 +461,25 @@ class BasicTransformerBlock(nn.Module):
             operations=operations,
         )
 
-        self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype))
+        num_ada_params = ADALN_CROSS_ATTN_PARAMS_COUNT if cross_attention_adaln else ADALN_BASE_PARAMS_COUNT
+        self.scale_shift_table = nn.Parameter(torch.empty(num_ada_params, dim, device=device, dtype=dtype))
 
-    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}, self_attention_mask=None):
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
+        if cross_attention_adaln:
+            self.prompt_scale_shift_table = nn.Parameter(torch.empty(2, dim, device=device, dtype=dtype))
 
-        attn1_input = comfy.ldm.common_dit.rms_norm(x)
-        attn1_input = torch.addcmul(attn1_input, attn1_input, scale_msa).add_(shift_msa)
-        attn1_input = self.attn1(attn1_input, pe=pe, mask=self_attention_mask, transformer_options=transformer_options)
-        x.addcmul_(attn1_input, gate_msa)
-        del attn1_input
+    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}, self_attention_mask=None, prompt_timestep=None):
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None, :6].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)[:, :, :6, :]).unbind(dim=2)
 
-        x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options)
+        x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe, mask=self_attention_mask, transformer_options=transformer_options) * gate_msa
+
+        if self.cross_attention_adaln:
+            shift_q_mca, scale_q_mca, gate_mca = (self.scale_shift_table[None, None, 6:9].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)[:, :, 6:9, :]).unbind(dim=2)
+            x += apply_cross_attention_adaln(
+                x, context, self.attn2, shift_q_mca, scale_q_mca, gate_mca,
+                self.prompt_scale_shift_table, prompt_timestep, attention_mask, transformer_options,
+            )
+        else:
+            x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options)
 
         y = comfy.ldm.common_dit.rms_norm(x)
         y = torch.addcmul(y, y, scale_mlp).add_(shift_mlp)
@@ -435,6 +487,47 @@ class BasicTransformerBlock(nn.Module):
 
         return x
 
+def compute_prompt_timestep(adaln_module, timestep_scaled, batch_size, hidden_dtype):
+    """Compute a single global prompt timestep for cross-attention ADaLN.
+
+    Uses the max across tokens (matching JAX max_per_segment) and broadcasts
+    over text tokens.  Returns None when *adaln_module* is None.
+    """
+    if adaln_module is None:
+        return None
+    ts_input = (
+        timestep_scaled.max(dim=1, keepdim=True).values.flatten()
+        if timestep_scaled.dim() > 1
+        else timestep_scaled.flatten()
+    )
+    prompt_ts, _ = adaln_module(
+        ts_input,
+        {"resolution": None, "aspect_ratio": None},
+        batch_size=batch_size,
+        hidden_dtype=hidden_dtype,
+    )
+    return prompt_ts.view(batch_size, 1, prompt_ts.shape[-1])
+
+
+def apply_cross_attention_adaln(
+    x, context, attn, q_shift, q_scale, q_gate,
+    prompt_scale_shift_table, prompt_timestep,
+    attention_mask=None, transformer_options={},
+):
+    """Apply cross-attention with ADaLN modulation (shift/scale/gate on Q and KV).
+
+    Q params (q_shift, q_scale, q_gate) are pre-extracted by the caller so
+    that both regular tensors and CompressedTimestep are supported.
+    """
+    batch_size = x.shape[0]
+    shift_kv, scale_kv = (
+        prompt_scale_shift_table[None, None].to(device=x.device, dtype=x.dtype)
+        + prompt_timestep.reshape(batch_size, prompt_timestep.shape[1], 2, -1)
+    ).unbind(dim=2)
+    attn_input = comfy.ldm.common_dit.rms_norm(x) * (1 + q_scale) + q_shift
+    encoder_hidden_states = context * (1 + scale_kv) + shift_kv
+    return attn(attn_input, context=encoder_hidden_states, mask=attention_mask, transformer_options=transformer_options) * q_gate
+
 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)})'
@@ -556,6 +649,9 @@ class LTXBaseModel(torch.nn.Module, ABC):
         vae_scale_factors: tuple = (8, 32, 32),
         use_middle_indices_grid=False,
         timestep_scale_multiplier = 1000.0,
+        caption_proj_before_connector=False,
+        cross_attention_adaln=False,
+        caption_projection_first_linear=True,
         dtype=None,
         device=None,
         operations=None,
@@ -582,6 +678,9 @@ class LTXBaseModel(torch.nn.Module, ABC):
         self.causal_temporal_positioning = causal_temporal_positioning
         self.operations = operations
         self.timestep_scale_multiplier = timestep_scale_multiplier
+        self.caption_proj_before_connector = caption_proj_before_connector
+        self.cross_attention_adaln = cross_attention_adaln
+        self.caption_projection_first_linear = caption_projection_first_linear
 
         # Common dimensions
         self.inner_dim = num_attention_heads * attention_head_dim
@@ -609,17 +708,37 @@ class LTXBaseModel(torch.nn.Module, ABC):
             self.in_channels, self.inner_dim, bias=True, dtype=dtype, device=device
         )
 
+        embedding_coefficient = ADALN_CROSS_ATTN_PARAMS_COUNT if self.cross_attention_adaln else ADALN_BASE_PARAMS_COUNT
         self.adaln_single = AdaLayerNormSingle(
-            self.inner_dim, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations
+            self.inner_dim, embedding_coefficient=embedding_coefficient, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations
         )
 
-        self.caption_projection = PixArtAlphaTextProjection(
-            in_features=self.caption_channels,
-            hidden_size=self.inner_dim,
-            dtype=dtype,
-            device=device,
-            operations=self.operations,
-        )
+        if self.cross_attention_adaln:
+            self.prompt_adaln_single = AdaLayerNormSingle(
+                self.inner_dim, embedding_coefficient=2, use_additional_conditions=False, dtype=dtype, device=device, operations=self.operations
+            )
+        else:
+            self.prompt_adaln_single = None
+
+        if self.caption_proj_before_connector:
+            if self.caption_projection_first_linear:
+                self.caption_projection = NormSingleLinearTextProjection(
+                    in_features=self.caption_channels,
+                    hidden_size=self.inner_dim,
+                    dtype=dtype,
+                    device=device,
+                    operations=self.operations,
+                )
+            else:
+                self.caption_projection = lambda a: a
+        else:
+            self.caption_projection = PixArtAlphaTextProjection(
+                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):
@@ -665,9 +784,9 @@ class LTXBaseModel(torch.nn.Module, ABC):
         if grid_mask is not None:
             timestep = timestep[:, grid_mask]
 
-        timestep = timestep * self.timestep_scale_multiplier
+        timestep_scaled = timestep * self.timestep_scale_multiplier
         timestep, embedded_timestep = self.adaln_single(
-            timestep.flatten(),
+            timestep_scaled.flatten(),
             {"resolution": None, "aspect_ratio": None},
             batch_size=batch_size,
             hidden_dtype=hidden_dtype,
@@ -677,14 +796,18 @@ class LTXBaseModel(torch.nn.Module, ABC):
         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
+        prompt_timestep = compute_prompt_timestep(
+            self.prompt_adaln_single, timestep_scaled, batch_size, hidden_dtype
+        )
+
+        return timestep, embedded_timestep, prompt_timestep
 
     def _prepare_context(self, context, batch_size, x, attention_mask=None):
         """Prepare context for transformer blocks."""
-        if self.caption_projection is not None:
+        if self.caption_proj_before_connector is False:
             context = self.caption_projection(context)
-            context = context.view(batch_size, -1, x.shape[-1])
 
+        context = context.view(batch_size, -1, x.shape[-1])
         return context, attention_mask
 
     def _precompute_freqs_cis(
@@ -792,7 +915,8 @@ class LTXBaseModel(torch.nn.Module, ABC):
         merged_args.update(additional_args)
 
         # Prepare timestep and context
-        timestep, embedded_timestep = self._prepare_timestep(timestep, batch_size, input_dtype, **merged_args)
+        timestep, embedded_timestep, prompt_timestep = self._prepare_timestep(timestep, batch_size, input_dtype, **merged_args)
+        merged_args["prompt_timestep"] = prompt_timestep
         context, attention_mask = self._prepare_context(context, batch_size, x, attention_mask)
 
         # Prepare attention mask and positional embeddings
@@ -833,7 +957,9 @@ class LTXVModel(LTXBaseModel):
         causal_temporal_positioning=False,
         vae_scale_factors=(8, 32, 32),
         use_middle_indices_grid=False,
-        timestep_scale_multiplier = 1000.0,
+        timestep_scale_multiplier=1000.0,
+        caption_proj_before_connector=False,
+        cross_attention_adaln=False,
         dtype=None,
         device=None,
         operations=None,
@@ -852,6 +978,8 @@ class LTXVModel(LTXBaseModel):
             vae_scale_factors=vae_scale_factors,
             use_middle_indices_grid=use_middle_indices_grid,
             timestep_scale_multiplier=timestep_scale_multiplier,
+            caption_proj_before_connector=caption_proj_before_connector,
+            cross_attention_adaln=cross_attention_adaln,
             dtype=dtype,
             device=device,
             operations=operations,
@@ -860,7 +988,6 @@ class LTXVModel(LTXBaseModel):
 
     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):
@@ -872,6 +999,7 @@ class LTXVModel(LTXBaseModel):
                     self.num_attention_heads,
                     self.attention_head_dim,
                     context_dim=self.cross_attention_dim,
+                    cross_attention_adaln=self.cross_attention_adaln,
                     dtype=dtype,
                     device=device,
                     operations=self.operations,
@@ -1149,16 +1277,17 @@ class LTXVModel(LTXBaseModel):
         """Process transformer blocks for LTXV."""
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
+        prompt_timestep = kwargs.get("prompt_timestep", None)
 
         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"], self_attention_mask=args.get("self_attention_mask"))
+                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"], self_attention_mask=args.get("self_attention_mask"), prompt_timestep=args.get("prompt_timestep"))
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe, "transformer_options": transformer_options, "self_attention_mask": self_attention_mask}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe, "transformer_options": transformer_options, "self_attention_mask": self_attention_mask, "prompt_timestep": prompt_timestep}, {"original_block": block_wrap})
                 x = out["img"]
             else:
                 x = block(
@@ -1169,6 +1298,7 @@ class LTXVModel(LTXBaseModel):
                     pe=pe,
                     transformer_options=transformer_options,
                     self_attention_mask=self_attention_mask,
+                    prompt_timestep=prompt_timestep,
                 )
 
         return x

comfy/ldm/lightricks/vae/audio_vae.py

@@ -13,7 +13,7 @@ from comfy.ldm.lightricks.vae.causal_audio_autoencoder import (
     CausalityAxis,
     CausalAudioAutoencoder,
 )
-from comfy.ldm.lightricks.vocoders.vocoder import Vocoder
+from comfy.ldm.lightricks.vocoders.vocoder import Vocoder, VocoderWithBWE
 
 LATENT_DOWNSAMPLE_FACTOR = 4
 
@@ -141,7 +141,10 @@ class AudioVAE(torch.nn.Module):
         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)
+        if "bwe" in component_config.vocoder:
+            self.vocoder = VocoderWithBWE(config=component_config.vocoder)
+        else:
+            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)

comfy/ldm/lightricks/vae/causal_audio_autoencoder.py

@@ -822,26 +822,23 @@ class CausalAudioAutoencoder(nn.Module):
         super().__init__()
 
         if config is None:
-            config = self._guess_config()
+            config = self.get_default_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)),
+        self.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)
+        self.mel_hop_length = config.get("preprocessing", {}).get("stft", {}).get("hop_length", 160)
+        self.n_fft = 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)
+        causality_axis_value = encoder_config.get("causality_axis", CausalityAxis.HEIGHT.value)
         self.causality_axis = CausalityAxis.str_to_enum(causality_axis_value)
         self.is_causal = self.causality_axis == CausalityAxis.HEIGHT
 
@@ -850,44 +847,38 @@ class CausalAudioAutoencoder(nn.Module):
 
         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,
+    def get_default_config(self):
+        ddconfig = {
             "double_z": True,
-            "attn_type": "vanilla",
-            "mid_block_add_attention": False,  # Based on metadata: false
+            "mel_bins": 64,
+            "z_channels": 8,
+            "resolution": 256,
+            "downsample_time": False,
+            "in_channels": 2,
+            "out_ch": 2,
+            "ch": 128,
+            "ch_mult": [1, 2, 4],
+            "num_res_blocks": 2,
+            "attn_resolutions": [],
+            "dropout": 0.0,
+            "mid_block_add_attention": 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,
+            "causality_axis": "height",
         }
 
         config = {
-            "_class_name": "CausalAudioAutoencoder",
-            "sampling_rate": 16000,
             "model": {
                 "params": {
-                    "encoder": encoder_config,
-                    "decoder": decoder_config,
+                    "ddconfig": ddconfig,
+                    "sampling_rate": 16000,
                 }
             },
+            "preprocessing": {
+                "stft": {
+                    "filter_length": 1024,
+                    "hop_length": 160,
+                },
+            },
         }
 
         return config

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -15,6 +15,9 @@ from comfy.ldm.modules.diffusionmodules.model import torch_cat_if_needed
 
 ops = comfy.ops.disable_weight_init
 
+def in_meta_context():
+    return torch.device("meta") == torch.empty(0).device
+
 def mark_conv3d_ended(module):
     tid = threading.get_ident()
     for _, m in module.named_modules():
@@ -350,6 +353,10 @@ class Decoder(nn.Module):
                 output_channel = output_channel * block_params.get("multiplier", 2)
             if block_name == "compress_all":
                 output_channel = output_channel * block_params.get("multiplier", 1)
+            if block_name == "compress_space":
+                output_channel = output_channel * block_params.get("multiplier", 1)
+            if block_name == "compress_time":
+                output_channel = output_channel * block_params.get("multiplier", 1)
 
         self.conv_in = make_conv_nd(
             dims,
@@ -395,17 +402,21 @@ class Decoder(nn.Module):
                     spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "compress_time":
+                output_channel = output_channel // block_params.get("multiplier", 1)
                 block = DepthToSpaceUpsample(
                     dims=dims,
                     in_channels=input_channel,
                     stride=(2, 1, 1),
+                    out_channels_reduction_factor=block_params.get("multiplier", 1),
                     spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "compress_space":
+                output_channel = output_channel // block_params.get("multiplier", 1)
                 block = DepthToSpaceUpsample(
                     dims=dims,
                     in_channels=input_channel,
                     stride=(1, 2, 2),
+                    out_channels_reduction_factor=block_params.get("multiplier", 1),
                     spatial_padding_mode=spatial_padding_mode,
                 )
             elif block_name == "compress_all":
@@ -455,6 +466,15 @@ class Decoder(nn.Module):
                 output_channel * 2, 0, operations=ops,
             )
             self.last_scale_shift_table = nn.Parameter(torch.empty(2, output_channel))
+        else:
+            self.register_buffer(
+                "last_scale_shift_table",
+                torch.tensor(
+                    [0.0, 0.0],
+                    device="cpu" if in_meta_context() else None
+                ).unsqueeze(1).expand(2, output_channel),
+                persistent=False,
+            )
 
 
     # def forward(self, sample: torch.FloatTensor, target_shape) -> torch.FloatTensor:
@@ -883,6 +903,15 @@ class ResnetBlock3D(nn.Module):
             self.scale_shift_table = nn.Parameter(
                 torch.randn(4, in_channels) / in_channels**0.5
             )
+        else:
+            self.register_buffer(
+                "scale_shift_table",
+                torch.tensor(
+                    [0.0, 0.0, 0.0, 0.0],
+                    device="cpu" if in_meta_context() else None
+                ).unsqueeze(1).expand(4, in_channels),
+                persistent=False,
+            )
 
         self.temporal_cache_state={}
 
@@ -1012,9 +1041,6 @@ class processor(nn.Module):
         super().__init__()
         self.register_buffer("std-of-means", torch.empty(128))
         self.register_buffer("mean-of-means", torch.empty(128))
-        self.register_buffer("mean-of-stds", torch.empty(128))
-        self.register_buffer("mean-of-stds_over_std-of-means", torch.empty(128))
-        self.register_buffer("channel", torch.empty(128))
 
     def un_normalize(self, x):
         return (x * self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)) + self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)
@@ -1027,9 +1053,12 @@ class VideoVAE(nn.Module):
         super().__init__()
 
         if config is None:
-            config = self.guess_config(version)
+            config = self.get_default_config(version)
 
+        self.config = config
         self.timestep_conditioning = config.get("timestep_conditioning", False)
+        self.decode_noise_scale = config.get("decode_noise_scale", 0.025)
+        self.decode_timestep = config.get("decode_timestep", 0.05)
         double_z = config.get("double_z", True)
         latent_log_var = config.get(
             "latent_log_var", "per_channel" if double_z else "none"
@@ -1044,6 +1073,7 @@ class VideoVAE(nn.Module):
             latent_log_var=latent_log_var,
             norm_layer=config.get("norm_layer", "group_norm"),
             spatial_padding_mode=config.get("spatial_padding_mode", "zeros"),
+            base_channels=config.get("encoder_base_channels", 128),
         )
 
         self.decoder = Decoder(
@@ -1051,6 +1081,7 @@ class VideoVAE(nn.Module):
             in_channels=config["latent_channels"],
             out_channels=config.get("out_channels", 3),
             blocks=config.get("decoder_blocks", config.get("decoder_blocks", config.get("blocks"))),
+            base_channels=config.get("decoder_base_channels", 128),
             patch_size=config.get("patch_size", 1),
             norm_layer=config.get("norm_layer", "group_norm"),
             causal=config.get("causal_decoder", False),
@@ -1060,7 +1091,7 @@ class VideoVAE(nn.Module):
 
         self.per_channel_statistics = processor()
 
-    def guess_config(self, version):
+    def get_default_config(self, version):
         if version == 0:
             config = {
                 "_class_name": "CausalVideoAutoencoder",
@@ -1167,8 +1198,7 @@ class VideoVAE(nn.Module):
         means, logvar = torch.chunk(self.encoder(x), 2, dim=1)
         return self.per_channel_statistics.normalize(means)
 
-    def decode(self, x, timestep=0.05, noise_scale=0.025):
+    def decode(self, x):
         if self.timestep_conditioning: #TODO: seed
-            x = torch.randn_like(x) * noise_scale + (1.0 - noise_scale) * x
-        return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=timestep)
-
+            x = torch.randn_like(x) * self.decode_noise_scale + (1.0 - self.decode_noise_scale) * x
+        return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=self.decode_timestep)

comfy/ldm/lightricks/vocoders/vocoder.py

@@ -3,6 +3,7 @@ import torch.nn.functional as F
 import torch.nn as nn
 import comfy.ops
 import numpy as np
+import math
 
 ops = comfy.ops.disable_weight_init
 
@@ -12,6 +13,307 @@ def get_padding(kernel_size, dilation=1):
     return int((kernel_size * dilation - dilation) / 2)
 
 
+# ---------------------------------------------------------------------------
+# Anti-aliased resampling helpers (kaiser-sinc filters) for BigVGAN v2
+# Adopted from https://github.com/NVIDIA/BigVGAN
+# ---------------------------------------------------------------------------
+
+
+def _sinc(x: torch.Tensor):
+    return torch.where(
+        x == 0,
+        torch.tensor(1.0, device=x.device, dtype=x.dtype),
+        torch.sin(math.pi * x) / math.pi / x,
+    )
+
+
+def kaiser_sinc_filter1d(cutoff, half_width, kernel_size):
+    even = kernel_size % 2 == 0
+    half_size = kernel_size // 2
+    delta_f = 4 * half_width
+    A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+    if A > 50.0:
+        beta = 0.1102 * (A - 8.7)
+    elif A >= 21.0:
+        beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0)
+    else:
+        beta = 0.0
+    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+    if even:
+        time = torch.arange(-half_size, half_size) + 0.5
+    else:
+        time = torch.arange(kernel_size) - half_size
+    if cutoff == 0:
+        filter_ = torch.zeros_like(time)
+    else:
+        filter_ = 2 * cutoff * window * _sinc(2 * cutoff * time)
+        filter_ /= filter_.sum()
+        filter = filter_.view(1, 1, kernel_size)
+    return filter
+
+
+class LowPassFilter1d(nn.Module):
+    def __init__(
+        self,
+        cutoff=0.5,
+        half_width=0.6,
+        stride=1,
+        padding=True,
+        padding_mode="replicate",
+        kernel_size=12,
+    ):
+        super().__init__()
+        if cutoff < -0.0:
+            raise ValueError("Minimum cutoff must be larger than zero.")
+        if cutoff > 0.5:
+            raise ValueError("A cutoff above 0.5 does not make sense.")
+        self.kernel_size = kernel_size
+        self.even = kernel_size % 2 == 0
+        self.pad_left = kernel_size // 2 - int(self.even)
+        self.pad_right = kernel_size // 2
+        self.stride = stride
+        self.padding = padding
+        self.padding_mode = padding_mode
+        filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+        self.register_buffer("filter", filter)
+
+    def forward(self, x):
+        _, C, _ = x.shape
+        if self.padding:
+            x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode)
+        return F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
+
+
+class UpSample1d(nn.Module):
+    def __init__(self, ratio=2, kernel_size=None, persistent=True, window_type="kaiser"):
+        super().__init__()
+        self.ratio = ratio
+        self.stride = ratio
+
+        if window_type == "hann":
+            # Hann-windowed sinc filter — identical to torchaudio.functional.resample
+            # with its default parameters (rolloff=0.99, lowpass_filter_width=6).
+            # Uses replicate boundary padding, matching the reference resampler exactly.
+            rolloff = 0.99
+            lowpass_filter_width = 6
+            width = math.ceil(lowpass_filter_width / rolloff)
+            self.kernel_size = 2 * width * ratio + 1
+            self.pad = width
+            self.pad_left = 2 * width * ratio
+            self.pad_right = self.kernel_size - ratio
+            t = (torch.arange(self.kernel_size) / ratio - width) * rolloff
+            t_clamped = t.clamp(-lowpass_filter_width, lowpass_filter_width)
+            window = torch.cos(t_clamped * math.pi / lowpass_filter_width / 2) ** 2
+            filter = (torch.sinc(t) * window * rolloff / ratio).view(1, 1, -1)
+        else:
+            # Kaiser-windowed sinc filter (BigVGAN default).
+            self.kernel_size = (
+                int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+            )
+            self.pad = self.kernel_size // ratio - 1
+            self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
+            self.pad_right = (
+                self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+            )
+            filter = kaiser_sinc_filter1d(
+                cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size
+            )
+
+        self.register_buffer("filter", filter, persistent=persistent)
+
+    def forward(self, x):
+        _, C, _ = x.shape
+        x = F.pad(x, (self.pad, self.pad), mode="replicate")
+        x = self.ratio * F.conv_transpose1d(
+            x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C
+        )
+        x = x[..., self.pad_left : -self.pad_right]
+        return x
+
+
+class DownSample1d(nn.Module):
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = (
+            int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        )
+        self.lowpass = LowPassFilter1d(
+            cutoff=0.5 / ratio,
+            half_width=0.6 / ratio,
+            stride=ratio,
+            kernel_size=self.kernel_size,
+        )
+
+    def forward(self, x):
+        return self.lowpass(x)
+
+
+class Activation1d(nn.Module):
+    def __init__(
+        self,
+        activation,
+        up_ratio=2,
+        down_ratio=2,
+        up_kernel_size=12,
+        down_kernel_size=12,
+    ):
+        super().__init__()
+        self.act = activation
+        self.upsample = UpSample1d(up_ratio, up_kernel_size)
+        self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+    def forward(self, x):
+        x = self.upsample(x)
+        x = self.act(x)
+        x = self.downsample(x)
+        return x
+
+
+# ---------------------------------------------------------------------------
+# BigVGAN v2 activations (Snake / SnakeBeta)
+# ---------------------------------------------------------------------------
+
+
+class Snake(nn.Module):
+    def __init__(
+        self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True
+    ):
+        super().__init__()
+        self.alpha_logscale = alpha_logscale
+        self.alpha = nn.Parameter(
+            torch.zeros(in_features)
+            if alpha_logscale
+            else torch.ones(in_features) * alpha
+        )
+        self.alpha.requires_grad = alpha_trainable
+        self.eps = 1e-9
+
+    def forward(self, x):
+        a = self.alpha.unsqueeze(0).unsqueeze(-1)
+        if self.alpha_logscale:
+            a = torch.exp(a)
+        return x + (1.0 / (a + self.eps)) * torch.sin(x * a).pow(2)
+
+
+class SnakeBeta(nn.Module):
+    def __init__(
+        self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True
+    ):
+        super().__init__()
+        self.alpha_logscale = alpha_logscale
+        self.alpha = nn.Parameter(
+            torch.zeros(in_features)
+            if alpha_logscale
+            else torch.ones(in_features) * alpha
+        )
+        self.alpha.requires_grad = alpha_trainable
+        self.beta = nn.Parameter(
+            torch.zeros(in_features)
+            if alpha_logscale
+            else torch.ones(in_features) * alpha
+        )
+        self.beta.requires_grad = alpha_trainable
+        self.eps = 1e-9
+
+    def forward(self, x):
+        a = self.alpha.unsqueeze(0).unsqueeze(-1)
+        b = self.beta.unsqueeze(0).unsqueeze(-1)
+        if self.alpha_logscale:
+            a = torch.exp(a)
+            b = torch.exp(b)
+        return x + (1.0 / (b + self.eps)) * torch.sin(x * a).pow(2)
+
+
+# ---------------------------------------------------------------------------
+# BigVGAN v2 AMPBlock (Anti-aliased Multi-Periodicity)
+# ---------------------------------------------------------------------------
+
+
+class AMPBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), activation="snake"):
+        super().__init__()
+        act_cls = SnakeBeta if activation == "snakebeta" else Snake
+        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),
+                ),
+            ]
+        )
+
+        self.acts1 = nn.ModuleList(
+            [Activation1d(act_cls(channels)) for _ in range(len(self.convs1))]
+        )
+        self.acts2 = nn.ModuleList(
+            [Activation1d(act_cls(channels)) for _ in range(len(self.convs2))]
+        )
+
+    def forward(self, x):
+        for c1, c2, a1, a2 in zip(self.convs1, self.convs2, self.acts1, self.acts2):
+            xt = a1(x)
+            xt = c1(xt)
+            xt = a2(xt)
+            xt = c2(xt)
+            x = x + xt
+        return x
+
+
+# ---------------------------------------------------------------------------
+# HiFi-GAN residual blocks
+# ---------------------------------------------------------------------------
+
+
 class ResBlock1(torch.nn.Module):
     def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
         super(ResBlock1, self).__init__()
@@ -119,6 +421,7 @@ class Vocoder(torch.nn.Module):
     """
     Vocoder model for synthesizing audio from spectrograms, based on: https://github.com/jik876/hifi-gan.
 
+    Supports both HiFi-GAN (resblock "1"/"2") and BigVGAN v2 (resblock "AMP1").
     """
 
     def __init__(self, config=None):
@@ -128,19 +431,39 @@ class Vocoder(torch.nn.Module):
             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])
+        upsample_rates = config.get("upsample_rates", [5, 4, 2, 2, 2])
+        upsample_kernel_sizes = config.get("upsample_kernel_sizes", [16, 16, 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")
+        activation = config.get("activation", "snake")
+        use_bias_at_final = config.get("use_bias_at_final", True)
 
+
+        # "output_sample_rate" is not present in recent checkpoint configs.
+        # When absent (None), AudioVAE.output_sample_rate computes it as:
+        #   sample_rate * vocoder.upsample_factor / mel_hop_length
+        # where upsample_factor = product of all upsample stride lengths,
+        # and mel_hop_length is loaded from the autoencoder config at
+        # preprocessing.stft.hop_length (see CausalAudioAutoencoder).
         self.output_sample_rate = config.get("output_sample_rate")
+        self.resblock = config.get("resblock", "1")
+        self.use_tanh_at_final = config.get("use_tanh_at_final", True)
+        self.apply_final_activation = config.get("apply_final_activation", True)
         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
+
+        if self.resblock == "1":
+            resblock_cls = ResBlock1
+        elif self.resblock == "2":
+            resblock_cls = ResBlock2
+        elif self.resblock == "AMP1":
+            resblock_cls = AMPBlock1
+        else:
+            raise ValueError(f"Unknown resblock type: {self.resblock}")
 
         self.ups = nn.ModuleList()
         for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
@@ -157,25 +480,40 @@ class Vocoder(torch.nn.Module):
         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))
+            for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes):
+                if self.resblock == "AMP1":
+                    self.resblocks.append(resblock_cls(ch, k, d, activation=activation))
+                else:
+                    self.resblocks.append(resblock_cls(ch, k, d))
 
         out_channels = 2 if stereo else 1
-        self.conv_post = ops.Conv1d(ch, out_channels, 7, 1, padding=3)
+        if self.resblock == "AMP1":
+            act_cls = SnakeBeta if activation == "snakebeta" else Snake
+            self.act_post = Activation1d(act_cls(ch))
+        else:
+            self.act_post = nn.LeakyReLU()
+
+        self.conv_post = ops.Conv1d(
+            ch, out_channels, 7, 1, padding=3, bias=use_bias_at_final
+        )
 
         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],
+            "upsample_rates": [5, 4, 2, 2, 2],
+            "upsample_kernel_sizes": [16, 16, 8, 4, 4],
             "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
             "upsample_initial_channel": 1024,
             "stereo": True,
             "resblock": "1",
+            "activation": "snake",
+            "use_bias_at_final": True,
+            "use_tanh_at_final": True,
         }
 
         return config
@@ -196,8 +534,10 @@ class Vocoder(torch.nn.Module):
             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)
+            if self.resblock != "AMP1":
+                x = F.leaky_relu(x, LRELU_SLOPE)
             x = self.ups[i](x)
             xs = None
             for j in range(self.num_kernels):
@@ -206,8 +546,167 @@ class Vocoder(torch.nn.Module):
                 else:
                     xs += self.resblocks[i * self.num_kernels + j](x)
             x = xs / self.num_kernels
-        x = F.leaky_relu(x)
+
+        x = self.act_post(x)
         x = self.conv_post(x)
-        x = torch.tanh(x)
+
+        if self.apply_final_activation:
+            if self.use_tanh_at_final:
+                x = torch.tanh(x)
+            else:
+                x = torch.clamp(x, -1, 1)
 
         return x
+
+
+class _STFTFn(nn.Module):
+    """Implements STFT as a convolution with precomputed DFT × Hann-window bases.
+
+    The DFT basis rows (real and imaginary parts interleaved) multiplied by the causal
+    Hann window are stored as buffers and loaded from the checkpoint. Using the exact
+    bfloat16 bases from training ensures the mel values fed to the BWE generator are
+    bit-identical to what it was trained on.
+    """
+
+    def __init__(self, filter_length: int, hop_length: int, win_length: int):
+        super().__init__()
+        self.hop_length = hop_length
+        self.win_length = win_length
+        n_freqs = filter_length // 2 + 1
+        self.register_buffer("forward_basis", torch.zeros(n_freqs * 2, 1, filter_length))
+        self.register_buffer("inverse_basis", torch.zeros(n_freqs * 2, 1, filter_length))
+
+    def forward(self, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        """Compute magnitude and phase spectrogram from a batch of waveforms.
+
+        Applies causal (left-only) padding of win_length - hop_length samples so that
+        each output frame depends only on past and present input — no lookahead.
+        The STFT is computed by convolving the padded signal with forward_basis.
+
+        Args:
+            y: Waveform tensor of shape (B, T).
+
+        Returns:
+            magnitude: Linear amplitude spectrogram, shape (B, n_freqs, T_frames).
+            phase:     Phase spectrogram in radians, shape (B, n_freqs, T_frames).
+                       Computed in float32 for numerical stability, then cast back to
+                       the input dtype.
+        """
+        if y.dim() == 2:
+            y = y.unsqueeze(1)                                # (B, 1, T)
+        left_pad = max(0, self.win_length - self.hop_length)  # causal: left-only
+        y = F.pad(y, (left_pad, 0))
+        spec = F.conv1d(y, self.forward_basis, stride=self.hop_length, padding=0)
+        n_freqs = spec.shape[1] // 2
+        real, imag = spec[:, :n_freqs], spec[:, n_freqs:]
+        magnitude = torch.sqrt(real ** 2 + imag ** 2)
+        phase = torch.atan2(imag.float(), real.float()).to(real.dtype)
+        return magnitude, phase
+
+
+class MelSTFT(nn.Module):
+    """Causal log-mel spectrogram module whose buffers are loaded from the checkpoint.
+
+    Computes a log-mel spectrogram by running the causal STFT (_STFTFn) on the input
+    waveform and projecting the linear magnitude spectrum onto the mel filterbank.
+
+    The module's state dict layout matches the 'mel_stft.*' keys stored in the checkpoint
+    (mel_basis, stft_fn.forward_basis, stft_fn.inverse_basis).
+    """
+
+    def __init__(
+        self,
+        filter_length: int,
+        hop_length: int,
+        win_length: int,
+        n_mel_channels: int,
+        sampling_rate: int,
+        mel_fmin: float,
+        mel_fmax: float,
+    ):
+        super().__init__()
+        self.stft_fn = _STFTFn(filter_length, hop_length, win_length)
+
+        n_freqs = filter_length // 2 + 1
+        self.register_buffer("mel_basis", torch.zeros(n_mel_channels, n_freqs))
+
+    def mel_spectrogram(
+        self, y: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Compute log-mel spectrogram and auxiliary spectral quantities.
+
+        Args:
+            y: Waveform tensor of shape (B, T).
+
+        Returns:
+            log_mel:   Log-compressed mel spectrogram, shape (B, n_mel_channels, T_frames).
+                       Computed as log(clamp(mel_basis @ magnitude, min=1e-5)).
+            magnitude: Linear amplitude spectrogram, shape (B, n_freqs, T_frames).
+            phase:     Phase spectrogram in radians, shape (B, n_freqs, T_frames).
+            energy:    Per-frame energy (L2 norm over frequency), shape (B, T_frames).
+        """
+        magnitude, phase = self.stft_fn(y)
+        energy = torch.norm(magnitude, dim=1)
+        mel = torch.matmul(self.mel_basis.to(magnitude.dtype), magnitude)
+        log_mel = torch.log(torch.clamp(mel, min=1e-5))
+        return log_mel, magnitude, phase, energy
+
+
+class VocoderWithBWE(torch.nn.Module):
+    """Vocoder with bandwidth extension (BWE) for higher sample rate output.
+
+    Chains a base vocoder (mel → low-rate waveform) with a BWE stage that upsamples
+    to a higher rate. The BWE computes a mel spectrogram from the low-rate waveform.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        vocoder_config = config["vocoder"]
+        bwe_config = config["bwe"]
+
+        self.vocoder = Vocoder(config=vocoder_config)
+        self.bwe_generator = Vocoder(
+            config={**bwe_config, "apply_final_activation": False}
+        )
+
+        self.input_sample_rate = bwe_config["input_sampling_rate"]
+        self.output_sample_rate = bwe_config["output_sampling_rate"]
+        self.hop_length = bwe_config["hop_length"]
+
+        self.mel_stft = MelSTFT(
+            filter_length=bwe_config["n_fft"],
+            hop_length=bwe_config["hop_length"],
+            win_length=bwe_config["n_fft"],
+            n_mel_channels=bwe_config["num_mels"],
+            sampling_rate=bwe_config["input_sampling_rate"],
+            mel_fmin=0.0,
+            mel_fmax=bwe_config["input_sampling_rate"] / 2.0,
+        )
+        self.resampler = UpSample1d(
+            ratio=bwe_config["output_sampling_rate"] // bwe_config["input_sampling_rate"],
+            persistent=False,
+            window_type="hann",
+        )
+
+    def _compute_mel(self, audio):
+        """Compute log-mel spectrogram from waveform using causal STFT bases."""
+        B, C, T = audio.shape
+        flat = audio.reshape(B * C, -1)                         # (B*C, T)
+        mel, _, _, _ = self.mel_stft.mel_spectrogram(flat)      # (B*C, n_mels, T_frames)
+        return mel.reshape(B, C, mel.shape[1], mel.shape[2])    # (B, C, n_mels, T_frames)
+
+    def forward(self, mel_spec):
+        x = self.vocoder(mel_spec)
+        _, _, T_low = x.shape
+        T_out = T_low * self.output_sample_rate // self.input_sample_rate
+
+        remainder = T_low % self.hop_length
+        if remainder != 0:
+            x = F.pad(x, (0, self.hop_length - remainder))
+
+        mel = self._compute_mel(x)
+        residual = self.bwe_generator(mel)
+        skip = self.resampler(x)
+        assert residual.shape == skip.shape, f"residual {residual.shape} != skip {skip.shape}"
+
+        return torch.clamp(residual + skip, -1, 1)[..., :T_out]

comfy/ldm/lumina/model.py

@@ -14,6 +14,7 @@ from comfy.ldm.flux.layers import EmbedND
 from comfy.ldm.flux.math import apply_rope
 import comfy.patcher_extension
 import comfy.utils
+from comfy.ldm.chroma_radiance.layers import NerfEmbedder
 
 
 def invert_slices(slices, length):
@@ -858,3 +859,267 @@ class NextDiT(nn.Module):
         img = self.unpatchify(img, img_size, cap_size, return_tensor=x_is_tensor)[:, :, :h, :w]
         return -img
 
+
+#############################################################################
+#                        Pixel Space Decoder Components                     #
+#############################################################################
+
+def _modulate_shift_scale(x, shift, scale):
+    return x * (1 + scale) + shift
+
+
+class PixelResBlock(nn.Module):
+    """
+    Residual block with AdaLN modulation, zero-initialised so it starts as
+    an identity at the beginning of training.
+    """
+
+    def __init__(self, channels: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.in_ln = operations.LayerNorm(channels, eps=1e-6, dtype=dtype, device=device)
+        self.mlp = nn.Sequential(
+            operations.Linear(channels, channels, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(channels, channels, bias=True, dtype=dtype, device=device),
+        )
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(channels, 3 * channels, bias=True, dtype=dtype, device=device),
+        )
+
+    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        shift, scale, gate = self.adaLN_modulation(y).chunk(3, dim=-1)
+        h = _modulate_shift_scale(self.in_ln(x), shift, scale)
+        h = self.mlp(h)
+        return x + gate * h
+
+
+class DCTFinalLayer(nn.Module):
+    """Zero-initialised output projection (adopted from DiT)."""
+
+    def __init__(self, model_channels: int, out_channels: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(model_channels, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(model_channels, out_channels, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.linear(self.norm_final(x))
+
+
+class SimpleMLPAdaLN(nn.Module):
+    """
+    Small MLP decoder head for the pixel-space variant.
+
+    Takes per-patch pixel values and a per-patch conditioning vector from the
+    transformer backbone and predicts the denoised pixel values.
+
+    x : [B*N, P^2, C]   – noisy pixel values per patch position
+    c : [B*N, dim]       – backbone hidden state per patch (conditioning)
+    → [B*N, P^2, C]
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        model_channels: int,
+        out_channels: int,
+        z_channels: int,
+        num_res_blocks: int,
+        max_freqs: int = 8,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.dtype = dtype
+
+        # Project backbone hidden state → per-patch conditioning
+        self.cond_embed = operations.Linear(z_channels, model_channels, dtype=dtype, device=device)
+
+        # Input projection with DCT positional encoding
+        self.input_embedder = NerfEmbedder(
+            in_channels=in_channels,
+            hidden_size_input=model_channels,
+            max_freqs=max_freqs,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        # Residual blocks
+        self.res_blocks = nn.ModuleList([
+            PixelResBlock(model_channels, dtype=dtype, device=device, operations=operations) for _ in range(num_res_blocks)
+        ])
+
+        # Output projection
+        self.final_layer = DCTFinalLayer(model_channels, out_channels, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+        # x: [B*N, 1, P^2*C],  c: [B*N, dim]
+        original_dtype = x.dtype
+        weight_dtype = self.cond_embed.weight.dtype if hasattr(self.cond_embed, "weight") and self.cond_embed.weight is not None else (self.dtype or x.dtype)
+        x = self.input_embedder(x)                                   # [B*N, 1, model_channels]
+        y = self.cond_embed(c.to(weight_dtype)).unsqueeze(1)         # [B*N, 1, model_channels]
+        x = x.to(weight_dtype)
+        for block in self.res_blocks:
+            x = block(x, y)
+        return self.final_layer(x).to(original_dtype)                # [B*N, 1, P^2*C]
+
+
+#############################################################################
+#                          NextDiT – Pixel Space                            #
+#############################################################################
+
+class NextDiTPixelSpace(NextDiT):
+    """
+    Pixel-space variant of NextDiT.
+
+    Identical transformer backbone to NextDiT, but the output head is replaced
+    with a small MLP decoder (SimpleMLPAdaLN) that operates on raw pixel values
+    per patch rather than a single affine projection.
+
+    Key differences vs NextDiT:
+      • ``final_layer`` is removed; ``dec_net`` (SimpleMLPAdaLN) is used instead.
+      • ``_forward`` stores the raw patchified pixel values before the backbone
+        embedding and feeds them to ``dec_net`` together with the per-patch
+        backbone hidden states.
+      • Supports optional x0 prediction via ``use_x0``.
+    """
+
+    def __init__(
+        self,
+        # decoder-specific
+        decoder_hidden_size: int = 3840,
+        decoder_num_res_blocks: int = 4,
+        decoder_max_freqs: int = 8,
+        decoder_in_channels: int = None,  # full flattened patch size (patch_size^2 * in_channels)
+        use_x0: bool = False,
+        # all NextDiT args forwarded unchanged
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        # Remove the latent-space final layer – not used in pixel space
+        del self.final_layer
+
+        patch_size = kwargs.get("patch_size", 2)
+        in_channels = kwargs.get("in_channels", 4)
+        dim = kwargs.get("dim", 4096)
+
+        # decoder_in_channels is the full flattened patch: patch_size^2 * in_channels
+        dec_in_ch = decoder_in_channels if decoder_in_channels is not None else patch_size ** 2 * in_channels
+
+        self.dec_net = SimpleMLPAdaLN(
+            in_channels=dec_in_ch,
+            model_channels=decoder_hidden_size,
+            out_channels=dec_in_ch,
+            z_channels=dim,
+            num_res_blocks=decoder_num_res_blocks,
+            max_freqs=decoder_max_freqs,
+            dtype=kwargs.get("dtype"),
+            device=kwargs.get("device"),
+            operations=kwargs.get("operations"),
+        )
+
+        if use_x0:
+            self.register_buffer("__x0__", torch.tensor([]))
+
+    # ------------------------------------------------------------------
+    # Forward — mirrors NextDiT._forward exactly, replacing final_layer
+    # with the pixel-space dec_net decoder.
+    # ------------------------------------------------------------------
+    def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, ref_latents=[], ref_contexts=[], siglip_feats=[], transformer_options={}, **kwargs):
+        omni = len(ref_latents) > 0
+        if omni:
+            timesteps = torch.cat([timesteps * 0, timesteps], dim=0)
+
+        t = 1.0 - timesteps
+        cap_feats = context
+        cap_mask = attention_mask
+        bs, c, h, w = x.shape
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
+
+        t = self.t_embedder(t * self.time_scale, dtype=x.dtype)
+        adaln_input = t
+
+        if self.clip_text_pooled_proj is not None:
+            pooled = kwargs.get("clip_text_pooled", None)
+            if pooled is not None:
+                pooled = self.clip_text_pooled_proj(pooled)
+            else:
+                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))
+
+        # ---- capture raw pixel patches before patchify_and_embed embeds them ----
+        pH = pW = self.patch_size
+        B, C, H, W = x.shape
+        pixel_patches = (
+            x.view(B, C, H // pH, pH, W // pW, pW)
+             .permute(0, 2, 4, 3, 5, 1)   # [B, Ht, Wt, pH, pW, C]
+             .flatten(3)                   # [B, Ht, Wt, pH*pW*C]
+             .flatten(1, 2)               # [B, N, pH*pW*C]
+        )
+        N = pixel_patches.shape[1]
+        # decoder sees one token per patch: [B*N, 1, P^2*C]
+        pixel_values = pixel_patches.reshape(B * N, 1, pH * pW * C)
+
+        patches = transformer_options.get("patches", {})
+        x_is_tensor = isinstance(x, torch.Tensor)
+        img, mask, img_size, cap_size, freqs_cis, timestep_zero_index = self.patchify_and_embed(
+            x, cap_feats, cap_mask, adaln_input, num_tokens,
+            ref_latents=ref_latents, ref_contexts=ref_contexts,
+            siglip_feats=siglip_feats, transformer_options=transformer_options
+        )
+        freqs_cis = freqs_cis.to(img.device)
+
+        transformer_options["total_blocks"] = len(self.layers)
+        transformer_options["block_type"] = "double"
+        img_input = img
+        for i, layer in enumerate(self.layers):
+            transformer_options["block_index"] = i
+            img = layer(img, mask, freqs_cis, adaln_input, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options)
+            if "double_block" in patches:
+                for p in patches["double_block"]:
+                    out = p({"img": img[:, cap_size[0]:], "img_input": img_input[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options})
+                    if "img" in out:
+                        img[:, cap_size[0]:] = out["img"]
+                    if "txt" in out:
+                        img[:, :cap_size[0]] = out["txt"]
+
+        # ---- pixel-space decoder (replaces final_layer + unpatchify) ----
+        # img may have padding tokens beyond N; only the first N are real image patches
+        img_hidden = img[:, cap_size[0]:cap_size[0] + N, :]  # [B, N, dim]
+        decoder_cond = img_hidden.reshape(B * N, self.dim)    # [B*N, dim]
+
+        output = self.dec_net(pixel_values, decoder_cond)  # [B*N, 1, P^2*C]
+        output = output.reshape(B, N, -1)                  # [B, N, P^2*C]
+
+        # prepend zero cap placeholder so unpatchify indexing works unchanged
+        cap_placeholder = torch.zeros(
+            B, cap_size[0], output.shape[-1], device=output.device, dtype=output.dtype
+        )
+        img_out = self.unpatchify(
+            torch.cat([cap_placeholder, output], dim=1),
+            img_size, cap_size, return_tensor=x_is_tensor
+        )[:, :, :h, :w]
+
+        return -img_out
+
+    def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
+        # _forward returns neg_x0 = -x0 (negated decoder output).
+        #
+        # Reference inference (working_inference_reference.py):
+        #   out = _forward(img, t)          # = -x0
+        #   pred = (img - out) / t          # = (img + x0) / t  [_apply_x0_residual]
+        #   img += (t_prev - t_curr) * pred # Euler step
+        #
+        # ComfyUI's Euler sampler does the same:
+        #   x_next = x + (sigma_next - sigma) * model_output
+        # So model_output must equal pred = (x - neg_x0) / t = (x - (-x0)) / t = (x + x0) / t
+        neg_x0 = comfy.patcher_extension.WrapperExecutor.new_class_executor(
+            self._forward,
+            self,
+            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {}))
+        ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)
+
+        return (x - neg_x0) / timesteps.view(-1, 1, 1, 1)

comfy/model_base.py

@@ -1021,7 +1021,7 @@ class LTXAV(BaseModel):
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
             if hasattr(self.diffusion_model, "preprocess_text_embeds"):
-                cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype_inference()))
+                cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype_inference()), unprocessed=kwargs.get("unprocessed_ltxav_embeds", False))
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
 
         out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25))
@@ -1263,6 +1263,11 @@ class Lumina2(BaseModel):
             out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))])
         return out
 
+class ZImagePixelSpace(Lumina2):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        BaseModel.__init__(self, model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiTPixelSpace)
+        self.memory_usage_factor_conds = ("ref_latents",)
+
 class WAN21(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)

comfy/model_detection.py

@@ -423,7 +423,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["extra_per_block_abs_pos_emb_type"] = "learnable"
         return dit_config
 
-    if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys:  # Lumina 2
+    if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys and '{}noise_refiner.0.attention.k_norm.weight'.format(key_prefix) in state_dict_keys:  # Lumina 2
         dit_config = {}
         dit_config["image_model"] = "lumina2"
         dit_config["patch_size"] = 2
@@ -464,6 +464,29 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             if sig_weight is not None:
                 dit_config["siglip_feat_dim"] = sig_weight.shape[0]
 
+            dec_cond_key = '{}dec_net.cond_embed.weight'.format(key_prefix)
+            if dec_cond_key in state_dict_keys:  # pixel-space variant
+                dit_config["image_model"] = "zimage_pixel"
+                # patch_size and in_channels are derived from x_embedder:
+                #   x_embedder: Linear(patch_size * patch_size * in_channels, dim)
+                # The decoder also receives the full flat patch, so decoder_in_channels = x_embedder input dim.
+                x_emb_in = state_dict['{}x_embedder.weight'.format(key_prefix)].shape[1]
+                dec_out = state_dict['{}dec_net.final_layer.linear.weight'.format(key_prefix)].shape[0]
+                # patch_size: infer from decoder final layer output matching x_embedder input
+                # in_channels: infer from dec_net input_embedder (in_features = dec_in_ch + max_freqs^2)
+                embedder_w = state_dict['{}dec_net.input_embedder.embedder.0.weight'.format(key_prefix)]
+                dec_in_ch = dec_out  # decoder in == decoder out (same pixel space)
+                dit_config["patch_size"] = round((x_emb_in / 3) ** 0.5)  # assume RGB (in_channels=3)
+                dit_config["in_channels"] = 3
+                dit_config["decoder_in_channels"] = dec_in_ch
+                dit_config["decoder_hidden_size"] = state_dict[dec_cond_key].shape[0]
+                dit_config["decoder_num_res_blocks"] = count_blocks(
+                    state_dict_keys, '{}dec_net.res_blocks.'.format(key_prefix) + '{}.'
+                )
+                dit_config["decoder_max_freqs"] = int((embedder_w.shape[1] - dec_in_ch) ** 0.5)
+                if '{}__x0__'.format(key_prefix) in state_dict_keys:
+                    dit_config["use_x0"] = True
+
         return dit_config
 
     if '{}head.modulation'.format(key_prefix) in state_dict_keys:  # Wan 2.1
@@ -533,8 +556,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
         return dit_config
 
-    if f"{key_prefix}t_embedder.mlp.2.weight" in state_dict_keys:  # Hunyuan 3D 2.1
-
+    if f"{key_prefix}t_embedder.mlp.2.weight" in state_dict_keys and f"{key_prefix}blocks.0.attn1.k_norm.weight" in state_dict_keys:  # Hunyuan 3D 2.1
         dit_config = {}
         dit_config["image_model"] = "hunyuan3d2_1"
         dit_config["in_channels"] = state_dict[f"{key_prefix}x_embedder.weight"].shape[1]
@@ -1055,6 +1077,13 @@ def convert_diffusers_mmdit(state_dict, output_prefix=""):
     elif 'adaln_single.emb.timestep_embedder.linear_1.bias' in state_dict and 'pos_embed.proj.bias' in state_dict: # PixArt
         num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
         sd_map = comfy.utils.pixart_to_diffusers({"depth": num_blocks}, output_prefix=output_prefix)
+    elif 'noise_refiner.0.attention.norm_k.weight' in state_dict:
+        n_layers = count_blocks(state_dict, 'layers.{}.')
+        dim = state_dict['noise_refiner.0.attention.to_k.weight'].shape[0]
+        sd_map = comfy.utils.z_image_to_diffusers({"n_layers": n_layers, "dim": dim}, output_prefix=output_prefix)
+        for k in state_dict: # For zeta chroma
+            if k not in sd_map:
+                sd_map[k] = k
     elif 'x_embedder.weight' in state_dict: #Flux
         depth = count_blocks(state_dict, 'transformer_blocks.{}.')
         depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.')

comfy/model_management.py

@@ -32,9 +32,6 @@ import comfy.memory_management
 import comfy.utils
 import comfy.quant_ops
 
-import comfy_aimdo.torch
-import comfy_aimdo.model_vbar
-
 class VRAMState(Enum):
     DISABLED = 0    #No vram present: no need to move models to vram
     NO_VRAM = 1     #Very low vram: enable all the options to save vram
@@ -799,6 +796,8 @@ def archive_model_dtypes(model):
     for name, module in model.named_modules():
         for param_name, param in module.named_parameters(recurse=False):
             setattr(module, f"{param_name}_comfy_model_dtype", param.dtype)
+        for buf_name, buf in module.named_buffers(recurse=False):
+            setattr(module, f"{buf_name}_comfy_model_dtype", buf.dtype)
 
 
 def cleanup_models():
@@ -831,11 +830,14 @@ def unet_offload_device():
         return torch.device("cpu")
 
 def unet_inital_load_device(parameters, dtype):
+    cpu_dev = torch.device("cpu")
+    if comfy.memory_management.aimdo_enabled:
+        return cpu_dev
+
     torch_dev = get_torch_device()
     if vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.SHARED:
         return torch_dev
 
-    cpu_dev = torch.device("cpu")
     if DISABLE_SMART_MEMORY or vram_state == VRAMState.NO_VRAM:
         return cpu_dev
 
@@ -843,7 +845,7 @@ def unet_inital_load_device(parameters, dtype):
 
     mem_dev = get_free_memory(torch_dev)
     mem_cpu = get_free_memory(cpu_dev)
-    if mem_dev > mem_cpu and model_size < mem_dev and comfy.memory_management.aimdo_enabled:
+    if mem_dev > mem_cpu and model_size < mem_dev:
         return torch_dev
     else:
         return cpu_dev
@@ -946,6 +948,9 @@ def text_encoder_device():
         return torch.device("cpu")
 
 def text_encoder_initial_device(load_device, offload_device, model_size=0):
+    if comfy.memory_management.aimdo_enabled:
+        return offload_device
+
     if load_device == offload_device or model_size <= 1024 * 1024 * 1024:
         return offload_device
 
@@ -1206,43 +1211,6 @@ def cast_to_gathered(tensors, r, non_blocking=False, stream=None):
 
 
 def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None, r=None):
-    if hasattr(weight, "_v"):
-        #Unexpected usage patterns. There is no reason these don't work but they
-        #have no testing and no callers do this.
-        assert r is None
-        assert stream is None
-
-        cast_geometry = comfy.memory_management.tensors_to_geometries([ weight ])
-
-        if dtype is None:
-            dtype = weight._model_dtype
-
-        signature = comfy_aimdo.model_vbar.vbar_fault(weight._v)
-        if signature is not None:
-            if comfy_aimdo.model_vbar.vbar_signature_compare(signature, weight._v_signature):
-                v_tensor = weight._v_tensor
-            else:
-                raw_tensor = comfy_aimdo.torch.aimdo_to_tensor(weight._v, device)
-                v_tensor = comfy.memory_management.interpret_gathered_like(cast_geometry, raw_tensor)[0]
-                weight._v_tensor = v_tensor
-                weight._v_signature = signature
-                #Send it over
-                v_tensor.copy_(weight, non_blocking=non_blocking)
-            return v_tensor.to(dtype=dtype)
-
-        r = torch.empty_like(weight, dtype=dtype, device=device)
-
-        if weight.dtype != r.dtype and weight.dtype != weight._model_dtype:
-            #Offloaded casting could skip this, however it would make the quantizations
-            #inconsistent between loaded and offloaded weights. So force the double casting
-            #that would happen in regular flow to make offload deterministic.
-            cast_buffer = torch.empty_like(weight, dtype=weight._model_dtype, device=device)
-            cast_buffer.copy_(weight, non_blocking=non_blocking)
-            weight = cast_buffer
-        r.copy_(weight, non_blocking=non_blocking)
-
-        return r
-
     if device is None or weight.device == device:
         if not copy:
             if dtype is None or weight.dtype == dtype:
@@ -1698,12 +1666,16 @@ def lora_compute_dtype(device):
     return dtype
 
 def synchronize():
+    if cpu_mode():
+        return
     if is_intel_xpu():
         torch.xpu.synchronize()
     elif torch.cuda.is_available():
         torch.cuda.synchronize()
 
 def soft_empty_cache(force=False):
+    if cpu_mode():
+        return
     global cpu_state
     if cpu_state == CPUState.MPS:
         torch.mps.empty_cache()

comfy/model_patcher.py

@@ -241,6 +241,7 @@ class ModelPatcher:
 
         self.patches = {}
         self.backup = {}
+        self.backup_buffers = {}
         self.object_patches = {}
         self.object_patches_backup = {}
         self.weight_wrapper_patches = {}
@@ -306,10 +307,16 @@ class ModelPatcher:
         return self.model.lowvram_patch_counter
 
     def get_free_memory(self, device):
-        return comfy.model_management.get_free_memory(device)
+        #Prioritize batching (incl. CFG/conds etc) over keeping the model resident. In
+        #the vast majority of setups a little bit of offloading on the giant model more
+        #than pays for CFG. So return everything both torch and Aimdo could give us
+        aimdo_mem = 0
+        if comfy.memory_management.aimdo_enabled:
+            aimdo_mem = comfy_aimdo.model_vbar.vbars_analyze()
+        return comfy.model_management.get_free_memory(device) + aimdo_mem
 
     def get_clone_model_override(self):
-        return self.model, (self.backup, self.object_patches_backup, self.pinned)
+        return self.model, (self.backup, self.backup_buffers, self.object_patches_backup, self.pinned)
 
     def clone(self, disable_dynamic=False, model_override=None):
         class_ = self.__class__
@@ -336,7 +343,7 @@ class ModelPatcher:
 
         n.force_cast_weights = self.force_cast_weights
 
-        n.backup, n.object_patches_backup, n.pinned = model_override[1]
+        n.backup, n.backup_buffers, n.object_patches_backup, n.pinned = model_override[1]
 
         # attachments
         n.attachments = {}
@@ -698,7 +705,7 @@ class ModelPatcher:
         for key in list(self.pinned):
             self.unpin_weight(key)
 
-    def _load_list(self, prio_comfy_cast_weights=False, default_device=None):
+    def _load_list(self, for_dynamic=False, default_device=None):
         loading = []
         for n, m in self.model.named_modules():
             default = False
@@ -726,8 +733,13 @@ class ModelPatcher:
                         return 0
                     module_offload_mem += check_module_offload_mem("{}.weight".format(n))
                     module_offload_mem += check_module_offload_mem("{}.bias".format(n))
-                prepend = (not hasattr(m, "comfy_cast_weights"),) if prio_comfy_cast_weights else ()
-                loading.append(prepend + (module_offload_mem, module_mem, n, m, params))
+                # Dynamic: small weights (<64KB) first, then larger weights prioritized by size.
+                # Non-dynamic: prioritize by module offload cost.
+                if for_dynamic:
+                    sort_criteria = (module_offload_mem >= 64 * 1024, -module_offload_mem)
+                else:
+                    sort_criteria = (module_offload_mem,)
+                loading.append(sort_criteria + (module_mem, n, m, params))
         return loading
 
     def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False):
@@ -1435,10 +1447,6 @@ class ModelPatcherDynamic(ModelPatcher):
 
     def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):
         super().__init__(model, load_device, offload_device, size, weight_inplace_update)
-        #this is now way more dynamic and we dont support the same base model for both Dynamic
-        #and non-dynamic patchers.
-        if hasattr(self.model, "model_loaded_weight_memory"):
-            del self.model.model_loaded_weight_memory
         if not hasattr(self.model, "dynamic_vbars"):
             self.model.dynamic_vbars = {}
         self.non_dynamic_delegate_model = None
@@ -1461,15 +1469,7 @@ class ModelPatcherDynamic(ModelPatcher):
 
     def loaded_size(self):
         vbar = self._vbar_get()
-        if vbar is None:
-            return 0
-        return vbar.loaded_size()
-
-    def get_free_memory(self, device):
-        #NOTE: on high condition / batch counts, estimate should have already vacated
-        #all non-dynamic models so this is safe even if its not 100% true that this
-        #would all be avaiable for inference use.
-        return comfy.model_management.get_total_memory(device) - self.model_size()
+        return (vbar.loaded_size() if vbar is not None else 0) + self.model.model_loaded_weight_memory
 
     #Pinning is deferred to ops time. Assert against this API to avoid pin leaks.
 
@@ -1504,6 +1504,7 @@ class ModelPatcherDynamic(ModelPatcher):
 
         num_patches = 0
         allocated_size = 0
+        self.model.model_loaded_weight_memory = 0
 
         with self.use_ejected():
             self.unpatch_hooks()
@@ -1512,15 +1513,11 @@ class ModelPatcherDynamic(ModelPatcher):
             if vbar is not None:
                 vbar.prioritize()
 
-            #We force reserve VRAM for the non comfy-weight so we dont have to deal
-            #with pin and unpin syncrhonization which can be expensive for small weights
-            #with a high layer rate (e.g. autoregressive LLMs).
-            #prioritize the non-comfy weights (note the order reverse).
-            loading = self._load_list(prio_comfy_cast_weights=True, default_device=device_to)
-            loading.sort(reverse=True)
+            loading = self._load_list(for_dynamic=True, default_device=device_to)
+            loading.sort()
 
             for x in loading:
-                _, _, _, n, m, params = x
+                *_, module_mem, n, m, params = x
 
                 def set_dirty(item, dirty):
                     if dirty or not hasattr(item, "_v_signature"):
@@ -1558,6 +1555,9 @@ class ModelPatcherDynamic(ModelPatcher):
                     if key in self.backup:
                         comfy.utils.set_attr_param(self.model, key, self.backup[key].weight)
                     self.patch_weight_to_device(key, device_to=device_to)
+                    weight, _, _ = get_key_weight(self.model, key)
+                    if weight is not None:
+                        self.model.model_loaded_weight_memory += weight.numel() * weight.element_size()
 
                 if hasattr(m, "comfy_cast_weights"):
                     m.comfy_cast_weights = True
@@ -1583,21 +1583,26 @@ class ModelPatcherDynamic(ModelPatcher):
                     for param in params:
                         key = key_param_name_to_key(n, param)
                         weight, _, _ = get_key_weight(self.model, key)
-                        weight.seed_key = key
-                        set_dirty(weight, dirty)
-                        geometry = weight
-                        model_dtype = getattr(m, param + "_comfy_model_dtype", None) or weight.dtype
-                        geometry = comfy.memory_management.TensorGeometry(shape=weight.shape, dtype=model_dtype)
-                        weight_size = geometry.numel() * geometry.element_size()
-                        if vbar is not None and not hasattr(weight, "_v"):
-                            weight._v = vbar.alloc(weight_size)
-                            weight._model_dtype = model_dtype
-                        allocated_size += weight_size
-                    vbar.set_watermark_limit(allocated_size)
+                        if key not in self.backup:
+                            self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight, False)
+                        model_dtype = getattr(m, param + "_comfy_model_dtype", None)
+                        casted_weight = weight.to(dtype=model_dtype, device=device_to)
+                        comfy.utils.set_attr_param(self.model, key, casted_weight)
+                        self.model.model_loaded_weight_memory += casted_weight.numel() * casted_weight.element_size()
 
                 move_weight_functions(m, device_to)
 
-            logging.info(f"Model {self.model.__class__.__name__} prepared for dynamic VRAM loading. {allocated_size // (1024 ** 2)}MB Staged. {num_patches} patches attached.")
+            for key, buf in self.model.named_buffers(recurse=True):
+                if key not in self.backup_buffers:
+                    self.backup_buffers[key] = buf
+                module, buf_name = comfy.utils.resolve_attr(self.model, key)
+                model_dtype = getattr(module, buf_name + "_comfy_model_dtype", None)
+                casted_buf = buf.to(dtype=model_dtype, device=device_to)
+                comfy.utils.set_attr_buffer(self.model, key, casted_buf)
+                self.model.model_loaded_weight_memory += casted_buf.numel() * casted_buf.element_size()
+
+            force_load_stat = f" Force pre-loaded {len(self.backup)} weights: {self.model.model_loaded_weight_memory // 1024} KB." if len(self.backup) > 0 else ""
+            logging.info(f"Model {self.model.__class__.__name__} prepared for dynamic VRAM loading. {allocated_size // (1024 ** 2)}MB Staged. {num_patches} patches attached.{force_load_stat}")
 
             self.model.device = device_to
             self.model.current_weight_patches_uuid = self.patches_uuid
@@ -1613,12 +1618,23 @@ class ModelPatcherDynamic(ModelPatcher):
         assert self.load_device != torch.device("cpu")
 
         vbar = self._vbar_get()
-        return 0 if vbar is None else vbar.free_memory(memory_to_free)
+        freed = 0 if vbar is None else vbar.free_memory(memory_to_free)
+
+        if freed < memory_to_free:
+            for key in list(self.backup.keys()):
+                bk = self.backup.pop(key)
+                comfy.utils.set_attr_param(self.model, key, bk.weight)
+            for key in list(self.backup_buffers.keys()):
+                comfy.utils.set_attr_buffer(self.model, key, self.backup_buffers.pop(key))
+            freed += self.model.model_loaded_weight_memory
+            self.model.model_loaded_weight_memory = 0
+
+        return freed
 
     def partially_unload_ram(self, ram_to_unload):
-        loading = self._load_list(prio_comfy_cast_weights=True, default_device=self.offload_device)
+        loading = self._load_list(for_dynamic=True, default_device=self.offload_device)
         for x in loading:
-            _, _, _, _, m, _ = x
+            *_, m, _ = x
             ram_to_unload -= comfy.pinned_memory.unpin_memory(m)
             if ram_to_unload <= 0:
                 return
@@ -1640,11 +1656,6 @@ class ModelPatcherDynamic(ModelPatcher):
             for m in self.model.modules():
                 move_weight_functions(m, device_to)
 
-            keys = list(self.backup.keys())
-            for k in keys:
-                bk = self.backup[k]
-                comfy.utils.set_attr_param(self.model, k, bk.weight)
-
     def partially_load(self, device_to, extra_memory=0, force_patch_weights=False):
         assert not force_patch_weights #See above
         with self.use_ejected(skip_and_inject_on_exit_only=True):

comfy/ops.py

@@ -269,8 +269,8 @@ def uncast_bias_weight(s, weight, bias, offload_stream):
         return
     os, weight_a, bias_a = offload_stream
     device=None
-    #FIXME: This is not good RTTI
-    if not isinstance(weight_a, torch.Tensor):
+    #FIXME: This is really bad RTTI
+    if weight_a is not None and not isinstance(weight_a, torch.Tensor):
         comfy_aimdo.model_vbar.vbar_unpin(s._v)
         device = weight_a
     if os is None:
@@ -660,23 +660,29 @@ class fp8_ops(manual_cast):
 
 CUBLAS_IS_AVAILABLE = False
 try:
-    from cublas_ops import CublasLinear
+    from cublas_ops import CublasLinear, cublas_half_matmul
     CUBLAS_IS_AVAILABLE = True
 except ImportError:
     pass
 
 if CUBLAS_IS_AVAILABLE:
-    class cublas_ops(disable_weight_init):
-        class Linear(CublasLinear, disable_weight_init.Linear):
+    class cublas_ops(manual_cast):
+        class Linear(CublasLinear, manual_cast.Linear):
             def reset_parameters(self):
                 return None
 
             def forward_comfy_cast_weights(self, input):
-                return super().forward(input)
+                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+                x = cublas_half_matmul(input, weight, bias, self._epilogue_str, self.has_bias)
+                uncast_bias_weight(self, weight, bias, offload_stream)
+                return x
 
             def forward(self, *args, **kwargs):
-                return super().forward(*args, **kwargs)
-
+                run_every_op()
+                if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+                    return self.forward_comfy_cast_weights(*args, **kwargs)
+                else:
+                    return super().forward(*args, **kwargs)
 
 # ==============================================================================
 # Mixed Precision Operations

comfy/sd.py

@@ -428,7 +428,7 @@ class CLIP:
     def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):
         self.cond_stage_model.reset_clip_options()
 
-        self.load_model()
+        self.load_model(tokens)
         self.cond_stage_model.set_clip_options({"layer": None})
         self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
         return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)
@@ -1467,7 +1467,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             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.clip = comfy.text_encoders.lt.ltxav_te(**llama_detect(clip_data), **comfy.text_encoders.lt.sd_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:

comfy/supported_models.py

@@ -1118,6 +1118,20 @@ class ZImage(Lumina2):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_4b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.z_image.ZImageTokenizer, comfy.text_encoders.z_image.te(**hunyuan_detect))
 
+class ZImagePixelSpace(ZImage):
+    unet_config = {
+        "image_model": "zimage_pixel",
+    }
+
+    # Pixel-space model: no spatial compression, operates on raw RGB patches.
+    latent_format = latent_formats.ZImagePixelSpace
+
+    # Much lower memory than latent-space models (no VAE, small patches).
+    memory_usage_factor = 0.03 # TODO: figure out the optimal value for this.
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return model_base.ZImagePixelSpace(self, device=device)
+
 class WAN21_T2V(supported_models_base.BASE):
     unet_config = {
         "image_model": "wan2.1",
@@ -1720,6 +1734,6 @@ class LongCatImage(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.longcat_image.LongCatImageTokenizer, comfy.text_encoders.longcat_image.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, LongCatImage, 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, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
+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, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
 
 models += [SVD_img2vid]

comfy/text_encoders/lt.py

@@ -97,18 +97,39 @@ class Gemma3_12BModel(sd1_clip.SDClipModel):
         comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5)
         return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106])  # 106 is <end_of_turn>
 
+class DualLinearProjection(torch.nn.Module):
+    def __init__(self, in_dim, out_dim_video, out_dim_audio, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.audio_aggregate_embed = operations.Linear(in_dim, out_dim_audio, bias=True, dtype=dtype, device=device)
+        self.video_aggregate_embed = operations.Linear(in_dim, out_dim_video, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x):
+        source_dim = x.shape[-1]
+        x = x.movedim(1, -1)
+        x = (x * torch.rsqrt(torch.mean(x**2, dim=2, keepdim=True) + 1e-6)).flatten(start_dim=2)
+
+        video = self.video_aggregate_embed(x * math.sqrt(self.video_aggregate_embed.out_features / source_dim))
+        audio = self.audio_aggregate_embed(x * math.sqrt(self.audio_aggregate_embed.out_features / source_dim))
+        return torch.cat((video, audio), dim=-1)
+
 class LTXAVTEModel(torch.nn.Module):
-    def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_options={}):
+    def __init__(self, dtype_llama=None, device="cpu", dtype=None, text_projection_type="single_linear", model_options={}):
         super().__init__()
         self.dtypes = set()
         self.dtypes.add(dtype)
         self.compat_mode = False
+        self.text_projection_type = text_projection_type
 
         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)
+
+        if self.text_projection_type == "single_linear":
+            self.text_embedding_projection = operations.Linear(3840 * 49, 3840, bias=False, dtype=dtype, device=device)
+        elif self.text_projection_type == "dual_linear":
+            self.text_embedding_projection = DualLinearProjection(3840 * 49, 4096, 2048, dtype=dtype, device=device, operations=operations)
+
 
     def enable_compat_mode(self):  # TODO: remove
         from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector
@@ -148,18 +169,25 @@ class LTXAVTEModel(torch.nn.Module):
         out_device = out.device
         if comfy.model_management.should_use_bf16(self.execution_device):
             out = out.to(device=self.execution_device, dtype=torch.bfloat16)
-        out = out.movedim(1, -1).to(self.execution_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 = out.float()
 
-        if self.compat_mode:
-            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)
+        if self.text_projection_type == "single_linear":
+            out = out.movedim(1, -1).to(self.execution_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)
 
-        return out.to(out_device), pooled
+            if self.compat_mode:
+                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)
+                extra = {}
+            else:
+                extra = {"unprocessed_ltxav_embeds": True}
+        elif self.text_projection_type == "dual_linear":
+            out = self.text_embedding_projection(out)
+            extra = {"unprocessed_ltxav_embeds": True}
+
+        return out.to(device=out_device, dtype=torch.float), pooled, extra
 
     def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
         return self.gemma3_12b.generate(tokens["gemma3_12b"], do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed)
@@ -168,7 +196,7 @@ class LTXAVTEModel(torch.nn.Module):
         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"}, filter_keys=True)
+            sdo = comfy.utils.state_dict_prefix_replace(sd, {"text_embedding_projection.aggregate_embed.weight": "text_embedding_projection.weight", "text_embedding_projection.": "text_embedding_projection."}, filter_keys=True)
             if len(sdo) == 0:
                 sdo = sd
 
@@ -206,7 +234,7 @@ class LTXAVTEModel(torch.nn.Module):
         num_tokens = max(num_tokens, 642)
         return num_tokens * constant * 1024 * 1024
 
-def ltxav_te(dtype_llama=None, llama_quantization_metadata=None):
+def ltxav_te(dtype_llama=None, llama_quantization_metadata=None, text_projection_type="single_linear"):
     class LTXAVTEModel_(LTXAVTEModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
             if llama_quantization_metadata is not None:
@@ -214,9 +242,19 @@ def ltxav_te(dtype_llama=None, llama_quantization_metadata=None):
                 model_options["llama_quantization_metadata"] = llama_quantization_metadata
             if dtype_llama is not None:
                 dtype = dtype_llama
-            super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)
+            super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, text_projection_type=text_projection_type, model_options=model_options)
     return LTXAVTEModel_
 
+
+def sd_detect(state_dict_list, prefix=""):
+    for sd in state_dict_list:
+        if "{}text_embedding_projection.audio_aggregate_embed.bias".format(prefix) in sd:
+            return {"text_projection_type": "dual_linear"}
+        if "{}text_embedding_projection.weight".format(prefix) in sd or "{}text_embedding_projection.aggregate_embed.weight".format(prefix) in sd:
+            return {"text_projection_type": "single_linear"}
+    return {}
+
+
 def gemma3_te(dtype_llama=None, llama_quantization_metadata=None):
     class Gemma3_12BModel_(Gemma3_12BModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):

comfy/utils.py

@@ -869,20 +869,31 @@ def safetensors_header(safetensors_path, max_size=100*1024*1024):
 
 ATTR_UNSET={}
 
-def set_attr(obj, attr, value):
+def resolve_attr(obj, attr):
     attrs = attr.split(".")
     for name in attrs[:-1]:
         obj = getattr(obj, name)
-    prev = getattr(obj, attrs[-1], ATTR_UNSET)
+    return obj, attrs[-1]
+
+def set_attr(obj, attr, value):
+    obj, name = resolve_attr(obj, attr)
+    prev = getattr(obj, name, ATTR_UNSET)
     if value is ATTR_UNSET:
-        delattr(obj, attrs[-1])
+        delattr(obj, name)
     else:
-        setattr(obj, attrs[-1], value)
+        setattr(obj, name, value)
     return prev
 
 def set_attr_param(obj, attr, value):
     return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False))
 
+def set_attr_buffer(obj, attr, value):
+    obj, name = resolve_attr(obj, attr)
+    prev = getattr(obj, name, ATTR_UNSET)
+    persistent = name not in getattr(obj, "_non_persistent_buffers_set", set())
+    obj.register_buffer(name, value, persistent=persistent)
+    return prev
+
 def copy_to_param(obj, attr, value):
     # inplace update tensor instead of replacing it
     attrs = attr.split(".")

comfy_api/latest/_input_impl/video_types.py

@@ -401,6 +401,7 @@ class VideoFromComponents(VideoInput):
         codec: VideoCodec = VideoCodec.AUTO,
         metadata: Optional[dict] = None,
     ):
+        """Save the video to a file path or BytesIO buffer."""
         if format != VideoContainer.AUTO and format != VideoContainer.MP4:
             raise ValueError("Only MP4 format is supported for now")
         if codec != VideoCodec.AUTO and codec != VideoCodec.H264:
@@ -408,6 +409,10 @@ class VideoFromComponents(VideoInput):
         extra_kwargs = {}
         if isinstance(format, VideoContainer) and format != VideoContainer.AUTO:
             extra_kwargs["format"] = format.value
+        elif isinstance(path, io.BytesIO):
+            # BytesIO has no file extension, so av.open can't infer the format.
+            # Default to mp4 since that's the only supported format anyway.
+            extra_kwargs["format"] = "mp4"
         with av.open(path, mode='w', options={'movflags': 'use_metadata_tags'}, **extra_kwargs) as output:
             # Add metadata before writing any streams
             if metadata is not None:

comfy_api/latest/_io.py

@@ -1240,6 +1240,19 @@ class BoundingBox(ComfyTypeIO):
             return d
 
 
+@comfytype(io_type="CURVE")
+class Curve(ComfyTypeIO):
+    CurvePoint = tuple[float, float]
+    Type = list[CurvePoint]
+
+    class Input(WidgetInput):
+        def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
+                     socketless: bool=True, default: list[tuple[float, float]]=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced)
+            if default is None:
+                self.default = [(0.0, 0.0), (1.0, 1.0)]
+
+
 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
@@ -2226,5 +2239,6 @@ __all__ = [
     "PriceBadgeDepends",
     "PriceBadge",
     "BoundingBox",
+    "Curve",
     "NodeReplace",
 ]

comfy_extras/nodes_audio.py

@@ -96,7 +96,7 @@ class VAEEncodeAudio(IO.ComfyNode):
 
 def vae_decode_audio(vae, samples, tile=None, overlap=None):
     if tile is not None:
-        audio = vae.decode_tiled(samples["samples"], tile_y=tile, overlap=overlap).movedim(-1, 1)
+        audio = vae.decode_tiled(samples["samples"], tile_x=tile, tile_y=tile, overlap=overlap).movedim(-1, 1)
     else:
         audio = vae.decode(samples["samples"]).movedim(-1, 1)
 

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.15.1"
+__version__ = "0.16.0"

execution.py

@@ -876,12 +876,14 @@ async def validate_inputs(prompt_id, prompt, item, validated):
                 continue
         else:
             try:
-                # Unwraps values wrapped in __value__ key. This is used to pass
-                # list widget value to execution, as by default list value is
-                # reserved to represent the connection between nodes.
-                if isinstance(val, dict) and "__value__" in val:
-                    val = val["__value__"]
-                    inputs[x] = val
+                # Unwraps values wrapped in __value__ key or typed wrapper.
+                # This is used to pass list widget values to execution,
+                # as by default list value is reserved to represent the
+                # connection between nodes.
+                if isinstance(val, dict):
+                    if "__value__" in val:
+                        val = val["__value__"]
+                        inputs[x] = val
 
                 if input_type == "INT":
                     val = int(val)

main.py

@@ -16,11 +16,6 @@ from comfy_execution.progress import get_progress_state
 from comfy_execution.utils import get_executing_context
 from comfy_api import feature_flags
 
-import comfy_aimdo.control
-
-if enables_dynamic_vram():
-    comfy_aimdo.control.init()
-
 if __name__ == "__main__":
     #NOTE: These do not do anything on core ComfyUI, they are for custom nodes.
     os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
@@ -28,6 +23,11 @@ if __name__ == "__main__":
 
 setup_logger(log_level=args.verbose, use_stdout=args.log_stdout)
 
+import comfy_aimdo.control
+
+if enables_dynamic_vram():
+    comfy_aimdo.control.init()
+
 if os.name == "nt":
     os.environ['MIMALLOC_PURGE_DELAY'] = '0'
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.15.1"
+version = "0.16.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.39.19
-comfyui-workflow-templates==0.9.4
+comfyui-workflow-templates==0.9.7
 comfyui-embedded-docs==0.4.3
 torch
 torchsde
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy
 av>=14.2.0
 comfy-kitchen>=0.2.7
-comfy-aimdo>=0.2.4
+comfy-aimdo>=0.2.6
 requests
 
 #non essential dependencies: