Merge branch 'main' into self-refine-video

I'm not 100% sure on this, I initially tested this when I noticed the original code doesn't, but it's described in the paper... now I see the original code has added it too so it seems to be the intended way to use it.

LongVie2/modules.py

@@ -0,0 +1,199 @@
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+from ..wanvideo.modules.attention import attention
+
+def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor):
+    return (x * (1 + scale) + shift)
+
+
+def sinusoidal_embedding_1d(dim, position):
+    sinusoid = torch.outer(position.type(torch.float64), torch.pow(
+        10000, -torch.arange(dim//2, dtype=torch.float64, device=position.device).div(dim//2)))
+    x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
+    return x.to(position.dtype)
+
+
+def precompute_freqs_cis_3d(dim: int, end: int = 1024, theta: float = 10000.0):
+    # 3d rope precompute
+    f_freqs_cis = precompute_freqs_cis(dim - 2 * (dim // 3), end, theta)
+    h_freqs_cis = precompute_freqs_cis(dim // 3, end, theta)
+    w_freqs_cis = precompute_freqs_cis(dim // 3, end, theta)
+    return f_freqs_cis, h_freqs_cis, w_freqs_cis
+
+
+def precompute_freqs_cis(dim: int, end: int = 1024, theta: float = 10000.0):
+    # 1d rope precompute
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)
+                   [: (dim // 2)].double() / dim))
+    freqs = torch.outer(torch.arange(end, device=freqs.device), freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+    return freqs_cis
+
+
+def rope_apply(x, freqs, num_heads):
+    x = rearrange(x, "b s (n d) -> b s n d", n=num_heads)
+    x_out = torch.view_as_complex(x.to(torch.float64).reshape(
+        x.shape[0], x.shape[1], x.shape[2], -1, 2))
+    x_out = torch.view_as_real(x_out * freqs).flatten(2)
+    return x_out.to(x.dtype)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-5):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        dtype = x.dtype
+        return self.norm(x.float()).to(dtype) * self.weight
+
+
+class AttentionModule(nn.Module):
+    def __init__(self, num_heads, head_dim):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+    def forward(self, q, k, v):
+        b, n, d = q.size(0), self.num_heads, self.head_dim
+        x = attention(
+            q.view(b, -1, n, d),
+            k.view(b, -1, n, d),
+            v.view(b, -1, n, d)
+        )
+        return x.flatten(2)
+
+
+class SelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int, eps: float = 1e-6):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+
+        self.q = nn.Linear(dim, dim)
+        self.k = nn.Linear(dim, dim)
+        self.v = nn.Linear(dim, dim)
+        self.o = nn.Linear(dim, dim)
+        self.norm_q = RMSNorm(dim, eps=eps)
+        self.norm_k = RMSNorm(dim, eps=eps)
+
+        self.attn = AttentionModule(self.num_heads, self.head_dim)
+
+    def forward(self, x, freqs):
+        q = self.norm_q(self.q(x))
+        k = self.norm_k(self.k(x))
+        v = self.v(x)
+        q = rope_apply(q, freqs, self.num_heads)
+        k = rope_apply(k, freqs, self.num_heads)
+        x = self.attn(q, k, v)
+        return self.o(x)
+
+
+class CrossAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int, eps: float = 1e-6, clip_fea: torch.Tensor = None):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+
+        self.q = nn.Linear(dim, dim)
+        self.k = nn.Linear(dim, dim)
+        self.v = nn.Linear(dim, dim)
+        self.o = nn.Linear(dim, dim)
+        self.norm_q = RMSNorm(dim, eps=eps)
+        self.norm_k = RMSNorm(dim, eps=eps)
+
+
+        self.k_img = nn.Linear(dim, dim)
+        self.v_img = nn.Linear(dim, dim)
+        self.norm_k_img = RMSNorm(dim, eps=eps)
+
+        self.attn = AttentionModule(self.num_heads, self.head_dim)
+
+    def forward(self, x: torch.Tensor, y: torch.Tensor, clip_fea: torch.Tensor = None):
+        ctx = y
+        q = self.norm_q(self.q(x))
+        k = self.norm_k(self.k(ctx))
+        v = self.v(ctx)
+        x = self.attn(q, k, v)
+        if clip_fea is not None:
+            k_img = self.norm_k_img(self.k_img(clip_fea))
+            v_img = self.v_img(clip_fea)
+            y = self.attn(q, k_img, v_img)
+            x = x + y
+        return self.o(x)
+
+
+class GateModule(nn.Module):
+    def __init__(self,):
+        super().__init__()
+
+    def forward(self, x, gate, residual):
+        return x + gate * residual
+
+class DiTBlock(nn.Module):
+    def __init__(self, dim: int, num_heads: int, ffn_dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.ffn_dim = ffn_dim
+
+        self.self_attn = SelfAttention(dim, num_heads, eps)
+        self.cross_attn = CrossAttention(dim, num_heads, eps)
+        self.norm1 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False)
+        self.norm2 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False)
+        self.norm3 = nn.LayerNorm(dim, eps=eps)
+        self.ffn = nn.Sequential(nn.Linear(dim, ffn_dim), nn.GELU(
+            approximate='tanh'), nn.Linear(ffn_dim, dim))
+        self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
+        self.gate = GateModule()
+
+    def forward(self, x, context, t_mod, freqs, clip_fea=None):
+        has_seq = len(t_mod.shape) == 4
+        chunk_dim = 2 if has_seq else 1
+        # msa: multi-head self-attention  mlp: multi-layer perceptron
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+            self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(6, dim=chunk_dim)
+        if has_seq:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+                shift_msa.squeeze(2), scale_msa.squeeze(2), gate_msa.squeeze(2),
+                shift_mlp.squeeze(2), scale_mlp.squeeze(2), gate_mlp.squeeze(2),
+            )
+        input_x = modulate(self.norm1(x), shift_msa, scale_msa)
+        x = self.gate(x, gate_msa, self.self_attn(input_x, freqs))
+        x = x + self.cross_attn(self.norm3(x), context, clip_fea=clip_fea)
+        input_x = modulate(self.norm2(x), shift_mlp, scale_mlp)
+        x = self.gate(x, gate_mlp, self.ffn(input_x))
+        return x
+
+
+class WanModelDualControl(torch.nn.Module):
+    def __init__(self, dim: int, ffn_dim: int, eps: float, num_heads: int, control_layers = 12):
+        super().__init__()
+        self.control_layers = control_layers
+        self.control_blocks_dense = nn.ModuleList([
+            DiTBlock(dim//2, num_heads//2, ffn_dim//2, eps)
+            for _ in range(self.control_layers)
+        ])
+
+        self.control_blocks_sparse = nn.ModuleList([
+            DiTBlock(dim//2, num_heads//2, ffn_dim//2, eps)
+            for _ in range(self.control_layers)
+        ])
+
+        self.control_initial_combine_linear_dense = torch.nn.Linear(dim, dim//2)
+        self.control_initial_combine_linear_sparse = torch.nn.Linear(dim, dim//2)
+
+        self.control_text_linear = torch.nn.Linear(dim, dim//2)
+        self.control_t_mod = torch.nn.Linear(dim, dim//2)
+
+        self.control_combine_linears = torch.nn.ModuleList([torch.nn.Linear(dim//2, dim) for _ in range(self.control_layers)])
+        head_dim = dim // num_heads
+        self.freqs = precompute_freqs_cis_3d(head_dim)

LongVie2/nodes.py

@@ -0,0 +1,88 @@
+import torch
+from ..utils import log
+import comfy.model_management as mm
+
+device = mm.get_torch_device()
+offload_device = mm.unet_offload_device()
+
+class WanVideoAddDualControlEmbeds:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "embeds": ("WANVIDIMAGE_EMBEDS",),
+                    "vae": ("WANVAE", {"tooltip": "VAE model"}),
+                    "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "Strength of the reference embedding"}),
+                    "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percentage of the embedding application"}),
+                    "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percentage of the embedding application"}),
+                    "first_frame_noise_level": ("FLOAT", {"default": 0.925926, "min": 0.0, "max": 1.0, "step": 0.000001, "tooltip": "Noise level for the first frame when using previous frames"}),
+                },
+                "optional": {
+                    "dense": ("IMAGE", {"tooltip": "Dense control signal (depth) video input"}),
+                    "sparse": ("IMAGE", {"tooltip": "Sparse control signal (tracks) video input"}),
+                    "prev_images": ("IMAGE", {"tooltip": "Previous frames for temporal consistency, default is 8 frames"}),
+                }
+        }
+
+    RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",)
+    RETURN_NAMES = ("image_embeds",)
+    FUNCTION = "add"
+    CATEGORY = "WanVideoWrapper"
+
+    def add(self, embeds, vae, strength, start_percent, end_percent, first_frame_noise_level, dense=None, sparse=None, prev_images=None):
+        updated = dict(embeds)
+        updated.setdefault("dual_control", {})
+
+        if dense is None and sparse is None:
+            raise ValueError("At least one of dense or sparse inputs must be provided.")
+
+        num_frames = dense.shape[0] if dense is not None else sparse.shape[0]
+        height = dense.shape[1] if dense is not None else sparse.shape[1]
+        width = dense.shape[2] if dense is not None else sparse.shape[2]
+        msk = torch.ones(1, num_frames, height//8, width//8, device=device)
+        msk[:, 1:] = 0
+        msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
+        msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8)
+        msk = msk.transpose(1, 2)
+
+        dense_input_latent = sparse_input_latent = None
+
+        vae.to(device)
+        if dense is not None:
+            dense_images = 1 - dense[..., :3] # Invert colors for depth to match the usual range in comfy
+            dense_images = dense_images.permute(3, 0, 1, 2) * 2 - 1
+            dense_video_latent = vae.encode([dense_images.to(device, vae.dtype)], device, tiled=False)
+            dense_first = (dense_images[:, :1]).to(device, vae.dtype)
+            vae_input_dense = torch.cat([dense_first, torch.zeros(3, num_frames-1, height, width, device=device, dtype=vae.dtype)], dim=1)
+            dense_concat_latent = vae.encode([vae_input_dense], device, tiled=False)
+            dense_concat_latent = torch.cat([msk, dense_concat_latent], dim=1)
+            dense_input_latent = torch.cat([dense_video_latent, dense_concat_latent],dim=1)
+        if sparse is not None:
+            sparse_images = sparse[..., :3].permute(3, 0, 1, 2) * 2 - 1
+            sparse_video_latent = vae.encode([sparse_images.to(device, vae.dtype)], device, tiled=False)
+            sparse_first = (sparse_images[:, :1]).to(device, vae.dtype)
+            vae_input_sparse = torch.cat([sparse_first, torch.zeros(3, num_frames-1, height, width, device=device, dtype=vae.dtype)], dim=1)
+            sparse_concat_latent = vae.encode([vae_input_sparse], device, tiled=False)
+            sparse_concat_latent = torch.cat([msk, sparse_concat_latent], dim=1)
+            sparse_input_latent = torch.cat([sparse_video_latent, sparse_concat_latent],dim=1)
+
+        if prev_images is not None:
+            prev_images = prev_images[..., :3].permute(3, 0, 1, 2) * 2 - 1
+            prev_video_latent = vae.encode([prev_images.to(device, vae.dtype)], device, tiled=False)
+            updated["dual_control"]["prev_latent"] = prev_video_latent[0]
+
+        vae.to(offload_device)
+        updated["dual_control"]["dense_input_latent"] = dense_input_latent
+        updated["dual_control"]["sparse_input_latent"] = sparse_input_latent
+        updated["dual_control"]["strength"] = strength
+        updated["dual_control"]["start_percent"] = start_percent
+        updated["dual_control"]["end_percent"] = end_percent
+        updated["dual_control"]["first_frame_noise_level"] = first_frame_noise_level
+        return (updated,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "WanVideoAddDualControlEmbeds": WanVideoAddDualControlEmbeds,
+    }
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "WanVideoAddDualControlEmbeds": "WanVideo Add Dual Control Embeds",
+    }

__init__.py

@@ -49,6 +49,7 @@ OPTIONAL_MODULES = [
     (".WanMove.nodes", "WanMove"),
     (".SCAIL.nodes", "SCAIL"),
     (".LongCat.nodes", "LongCat"),
+    (".LongVie2.nodes", "LongVie2"),
 ]
 
 def register_nodes(module_path: str, name: str, optional: bool) -> None:
@@ -71,4 +72,4 @@ for module_path, name in REQUIRED_MODULES:
 for module_path, name in OPTIONAL_MODULES:
     register_nodes(module_path, name, optional=True)
 
-__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]

custom_linear.py

@@ -56,7 +56,7 @@ def _replace_linear(model, compute_dtype, state_dict, prefix="", patches=None, s
         module_prefix = module_prefix.replace("_orig_mod.", "")
         _replace_linear(module, compute_dtype, state_dict, module_prefix, patches, scale_weights, compile_args, modules_to_not_convert)
 
-        if isinstance(module, nn.Linear) and "loras" not in module_prefix and name not in modules_to_not_convert:
+        if isinstance(module, nn.Linear) and "loras" not in module_prefix and "dual_controller" not in module_prefix and name not in modules_to_not_convert:
             weight_key = module_prefix + "weight"
             if weight_key not in state_dict:
                 continue

multitalk/multitalk.py

@@ -42,41 +42,43 @@ def rotate_half(x):
     x = torch.stack((-x2, x1), dim=-1)
     return rearrange(x, "... d r -> ... (d r)")
 
-def calculate_x_ref_attn_map(visual_q, ref_k, ref_target_masks, mode='mean', attn_bias=None):
-    
-    ref_k = ref_k.to(visual_q.dtype).to(visual_q.device)
+def calculate_x_ref_attn_map(visual_q, ref_k, ref_target_masks, split_num=4):
     scale = 1.0 / visual_q.shape[-1] ** 0.5
-    visual_q = visual_q * scale
-    visual_q = visual_q.transpose(1, 2)
-    ref_k = ref_k.transpose(1, 2)
-    attn = visual_q @ ref_k.transpose(-2, -1)
-
-    if attn_bias is not None:
-        attn = attn + attn_bias
-
-    x_ref_attn_map_source = attn.softmax(-1) # B, H, x_seqlens, ref_seqlens
+    visual_q = visual_q.transpose(1, 2) * scale
 
+    B, H, x_seqlens, K = visual_q.shape
 
     x_ref_attn_maps = []
-    ref_target_masks = ref_target_masks.to(visual_q.dtype)
-    x_ref_attn_map_source = x_ref_attn_map_source.to(visual_q.dtype)
-
     for class_idx, ref_target_mask in enumerate(ref_target_masks):
-        ref_target_mask = ref_target_mask[None, None, None, ...]
-        x_ref_attnmap = x_ref_attn_map_source * ref_target_mask
-        x_ref_attnmap = x_ref_attnmap.sum(-1) / ref_target_mask.sum() # B, H, x_seqlens, ref_seqlens --> B, H, x_seqlens
-        x_ref_attnmap = x_ref_attnmap.permute(0, 2, 1) # B, x_seqlens, H
-       
-        if mode == 'mean':
-            x_ref_attnmap = x_ref_attnmap.mean(-1) # B, x_seqlens
-        elif mode == 'max':
-            x_ref_attnmap = x_ref_attnmap.max(-1) # B, x_seqlens
-        
-        x_ref_attn_maps.append(x_ref_attnmap)
-    
-    del attn, x_ref_attn_map_source
+        ref_target_mask = ref_target_mask.view(1, 1, 1, -1)
 
-    return torch.concat(x_ref_attn_maps, dim=0)
+        x_ref_attnmap = torch.zeros(B, H, x_seqlens, device=visual_q.device, dtype=visual_q.dtype)
+        chunk_size = min(max(x_seqlens // split_num, 1), x_seqlens)
+
+        for i in range(0, x_seqlens, chunk_size):
+            end_i = min(i + chunk_size, x_seqlens)
+
+            attn_chunk = visual_q[:, :, i:end_i] @ ref_k.permute(0, 2, 3, 1)  # B, H, chunk, ref_seqlens
+
+            # Apply softmax
+            attn_max = attn_chunk.max(dim=-1, keepdim=True).values
+            attn_chunk = (attn_chunk - attn_max).exp()
+            attn_sum = attn_chunk.sum(dim=-1, keepdim=True)
+            attn_chunk = attn_chunk / (attn_sum + 1e-8)
+
+            # Apply mask and sum
+            masked_attn = attn_chunk * ref_target_mask
+            x_ref_attnmap[:, :, i:end_i] = masked_attn.sum(-1) / (ref_target_mask.sum() + 1e-8)
+
+            del attn_chunk, masked_attn
+
+        # Average across heads
+        x_ref_attnmap = x_ref_attnmap.mean(dim=1)  # B, x_seqlens
+        x_ref_attn_maps.append(x_ref_attnmap)
+
+    del visual_q, ref_k
+
+    return torch.cat(x_ref_attn_maps, dim=0)
 
 def get_attn_map_with_target(visual_q, ref_k, shape, ref_target_masks=None, split_num=2):
     """Args:
@@ -129,27 +131,30 @@ class RotaryPositionalEmbedding1D(nn.Module):
             query with the same shape as input.
         """
         freqs_cis = self.precompute_freqs_cis_1d(pos_indices)
-
-        x_ = x.float()
+        in_dtype = x.dtype
+        x = x.float()
 
         freqs_cis = freqs_cis.float().to(x.device)
-        cos, sin = freqs_cis.cos(), freqs_cis.sin()
-        cos, sin = rearrange(cos, 'n d -> 1 1 n d'), rearrange(sin, 'n d -> 1 1 n d')
-        x_ = (x_ * cos) + (rotate_half(x_) * sin)
+        cos = rearrange(freqs_cis.cos(), 'n d -> 1 1 n d')
+        sin = rearrange(freqs_cis.sin(), 'n d -> 1 1 n d')
 
-        return x_.type_as(x)
+        # In-place rotation to save memory
+        x_rotated = rotate_half(x)
+        x.mul_(cos).add_(x_rotated * sin)
+
+        return x.to(in_dtype)
 
 class AudioProjModel(nn.Module):
     def __init__(
         self,
         seq_len=5,
-        seq_len_vf=12,
-        blocks=12,  
-        channels=768, 
+        seq_len_vf=8,
+        blocks=12,
+        channels=768,
         intermediate_dim=512,
         output_dim=768,
         context_tokens=32,
-        norm_output_audio=False,
+        norm_output_audio=True,
     ):
         super().__init__()
 
@@ -273,9 +278,9 @@ class SingleStreamMultiAttention(SingleStreamAttention):
     def __init__(
         self,
         dim: int,
-        encoder_hidden_states_dim: int,
         num_heads: int,
-        qkv_bias: bool,
+        qkv_bias: bool = True,
+        encoder_hidden_states_dim: int = 768,
         class_range: int = 24,
         class_interval: int = 4,
         attention_mode: str = 'sdpa',

multitalk/multitalk_loop.py

@@ -6,7 +6,7 @@ import numpy as np
 from ..latent_preview import prepare_callback
 from ..wanvideo.schedulers import get_scheduler
 from .multitalk import timestep_transform, add_noise
-from ..utils import log, print_memory, temporal_score_rescaling, offload_transformer, init_blockswap
+from ..utils import log, print_memory, temporal_score_rescaling, offload_transformer, init_blockswap, match_and_blend_colors
 from comfy.utils import load_torch_file
 from ..nodes_model_loading import load_weights
 from ..HuMo.nodes import get_audio_emb_window
@@ -48,7 +48,13 @@ def multitalk_loop(self, **kwargs):
     mode = image_embeds.get("multitalk_mode", "multitalk")
     if mode == "auto":
         mode = transformer.multitalk_model_type.lower()
+    elif mode == "skyreelsv3":
+        num_pseudo_frames = 5
+        pseudo_frames = reference_keyframes = None
+        keyframe_index = 0
+        reference_video = image_embeds.get("reference_video", None)
     log.info(f"Multitalk mode: {mode}")
+    drop_frames = image_embeds.get("drop_frames", 0)
     cond_frame = None
     offload = image_embeds.get("force_offload", False)
     offloaded = False
@@ -62,7 +68,9 @@ def multitalk_loop(self, **kwargs):
     motion_frame = image_embeds.get("motion_frame", 25)
     target_w = image_embeds.get("target_w", None)
     target_h = image_embeds.get("target_h", None)
-    original_images = cond_image = image_embeds.get("multitalk_start_image", None)
+    original_images = image_embeds.get("multitalk_start_image", None)
+    cond_image = original_images.clone() if original_images is not None else None
+    original_color_reference = cond_image.clone() if cond_image is not None else None
     if original_images is None:
         original_images = torch.zeros([noise.shape[0], 1, target_h, target_w], device=device)
 
@@ -94,7 +102,6 @@ def multitalk_loop(self, **kwargs):
     audio_embedding = multitalk_audio_embeds
     human_num = len(audio_embedding)
     audio_embs = None
-    cond_frame = None
 
     uni3c_data = None
     if uni3c_embeds is not None:
@@ -110,9 +117,56 @@ def multitalk_loop(self, **kwargs):
             log.warning("No encoded silence file found, padding with end of audio embedding instead.")
 
     total_frames = len(audio_embedding[0])
-    estimated_iterations = total_frames // (frame_num - motion_frame) + 1
+    estimated_iterations = total_frames // (frame_num - motion_frame - drop_frames) + 1
     callback = prepare_callback(patcher, estimated_iterations)
 
+    # If reference_video is provided, extract keyframes from it
+    if mode == "skyreelsv3" and reference_video is not None:
+        ref_video_length = reference_video.shape[1]  # (C, T, H, W)
+        if colormatch == "reinhard_torch":
+            reference_video = match_and_blend_colors(reference_video, original_color_reference, 1.0)
+
+        if ref_video_length >= total_frames:
+            # Reference is long enough - extract keyframes at the expected positions
+            segment_interval = frame_num - motion_frame - drop_frames
+            generate_idx = []
+            current_idx = frame_num - 1
+            while current_idx < total_frames:
+                generate_idx.append(min(current_idx, ref_video_length - 1))
+                current_idx += segment_interval
+        else:
+            # Calculate target indices then map to reference video
+            audio_length = total_frames
+            generate_idx_target = [0]
+            segment_interval = frame_num - motion_frame - drop_frames
+            current_idx = frame_num - 1
+            while current_idx < audio_length - 1:
+                generate_idx_target.append(current_idx)
+                current_idx += segment_interval
+            if generate_idx_target[-1] != audio_length - 1:
+                generate_idx_target.append(audio_length - 1)
+
+            # Map target indices to reference video
+            generate_idx_target = np.array(generate_idx_target, dtype=np.int16)
+            original_max = generate_idx_target[-1]
+            original_min = generate_idx_target[0]
+            if original_max > original_min:
+                generate_idx_float = (generate_idx_target.astype(np.float64) - original_min) * (ref_video_length - 1) / (original_max - original_min)
+                generate_idx = np.clip(np.round(generate_idx_float), 0, ref_video_length - 1).astype(np.int32).tolist()
+            else:
+                generate_idx = [0]
+
+            generate_idx = generate_idx[1:]
+            log.info(f"Reference video ({ref_video_length} frames) mapped to target ({total_frames} frames). Keyframe indices: {generate_idx}")
+
+        # Extract keyframes from reference video
+        # reference_video shape: (C, T, H, W) from nodes.py processing
+        # Select keyframes and add batch dimension: (C, num_keyframes, H, W) -> (1, C, num_keyframes, H, W)
+        selected_keyframes = reference_video[:, generate_idx]  # (C, num_keyframes, H, W)
+        reference_keyframes = selected_keyframes.unsqueeze(0).cpu()  # (1, C, num_keyframes, H, W)
+        log.info(f"Extracted {len(generate_idx)} keyframes from provided reference video at indices {generate_idx}, shape: {reference_keyframes.shape}")
+        log.info(f"Reference video total frames: {reference_video.shape[1]}, will generate {total_frames} total frames with {estimated_iterations} windows")
+
     if frame_num >= total_frames:
         arrive_last_frame = True
         estimated_iterations = 1
@@ -122,6 +176,14 @@ def multitalk_loop(self, **kwargs):
     while True: # start video generation iteratively
         self.cache_state = [None, None]
 
+        if mode == "skyreelsv3" and reference_keyframes is not None:
+            clamped_index = min(keyframe_index, reference_keyframes.shape[2] - 1) # Clamp keyframe_index to reuse last keyframe if we run out
+            pseudo_frames = reference_keyframes[:, :, clamped_index:clamped_index+1].repeat(1, 1, num_pseudo_frames, 1, 1) # Use one keyframe and repeat it 5 times
+            log.info(f"Window {iteration_count}: using keyframe {clamped_index}/{reference_keyframes.shape[2]-1} for pseudo frames.")
+            keyframe_index += 1
+        else:
+            pseudo_frames = None
+
         cur_motion_frames_latent_num = int(1 + (cur_motion_frames_num-1) // 4)
         if mode == "infinitetalk":
             cond_image = original_images[:, :, current_condframe_index:current_condframe_index+1] if cond_image is not None else None
@@ -133,15 +195,13 @@ def multitalk_loop(self, **kwargs):
                 center_indices = torch.clamp(center_indices, min=0, max=audio_embedding[human_idx].shape[0]-1)
                 audio_emb = audio_embedding[human_idx][center_indices].unsqueeze(0).to(device)
                 audio_embs.append(audio_emb)
-            audio_embs = torch.concat(audio_embs, dim=0).to(dtype)
+            audio_embs = torch.cat(audio_embs, dim=0).to(dtype)
 
         h, w = (cond_image.shape[-2], cond_image.shape[-1]) if cond_image is not None else (target_h, target_w)
         lat_h, lat_w = h // VAE_STRIDE[1], w // VAE_STRIDE[2]
         latent_frame_num = (frame_num - 1) // 4 + 1
 
-        noise = torch.randn(
-            16, latent_frame_num,
-            lat_h, lat_w, dtype=torch.float32, device=torch.device("cpu"), generator=seed_g).to(device)
+        noise = torch.randn(16, latent_frame_num, lat_h, lat_w, dtype=torch.float32, device=torch.device("cpu"), generator=seed_g).to(device)
 
         # Calculate the correct latent slice based on current iteration
         if is_first_clip:
@@ -198,24 +258,41 @@ def multitalk_loop(self, **kwargs):
         if cond_image is not None or cond_frame is not None:
             cond_ = cond_image if (is_first_clip or humo_image_cond is None) else cond_frame
             cond_frame_num = cond_.shape[2]
-            video_frames = torch.zeros(1, 3, frame_num-cond_frame_num, target_h, target_w, device=device, dtype=vae.dtype)
-            padding_frames_pixels_values = torch.concat([cond_.to(device, vae.dtype), video_frames], dim=2)
+
+            # Prepare pseudo frames if enabled and available from reference_video
+            if mode == "skyreelsv3" and pseudo_frames is not None:
+                video_frames = torch.zeros(1, 3, frame_num-cond_frame_num-num_pseudo_frames, target_h, target_w, device=device, dtype=vae.dtype)
+                padding_frames_pixels_values = torch.cat([cond_.to(device, vae.dtype), video_frames, pseudo_frames.to(device, vae.dtype)], dim=2)
+            else:
+                video_frames = torch.zeros(1, 3, frame_num-cond_frame_num, target_h, target_w, device=device, dtype=vae.dtype)
+                padding_frames_pixels_values = torch.cat([cond_.to(device, vae.dtype), video_frames], dim=2)
 
             # encode
             vae.to(device)
             y = vae.encode(padding_frames_pixels_values, device=device, tiled=tiled_vae, pbar=False).to(dtype)[0]
 
-            if mode == "multitalk":
-                latent_motion_frames = y[:, :cur_motion_frames_latent_num] # C T H W
-            else:
+            if mode == "infinitetalk":
                 cond_ = cond_image if is_first_clip else cond_frame
                 latent_motion_frames = vae.encode(cond_.to(device, vae.dtype), device=device, tiled=tiled_vae, pbar=False).to(dtype)[0]
+            else:
+                latent_motion_frames = y[:, :cur_motion_frames_latent_num] # C T H W
 
             vae.to(offload_device)
 
             #motion_frame_index = cur_motion_frames_latent_num if mode == "infinitetalk" else 1
-            msk = torch.zeros(4, latent_frame_num, lat_h, lat_w, device=device, dtype=dtype)
-            msk[:, :1] = 1
+            if mode == "skyreelsv3" and pseudo_frames is not None:
+                # create mask in pixel space, then transform
+                msk_pixel = torch.ones(1, frame_num, lat_h, lat_w, device=device)
+                msk_pixel[:, cur_motion_frames_num : -num_pseudo_frames] = 0
+                msk_pixel = torch.cat([
+                    torch.repeat_interleave(msk_pixel[:, 0:1], repeats=4, dim=1),
+                    msk_pixel[:, 1:],
+                ], dim=1)
+                msk_pixel = msk_pixel.view(1, msk_pixel.shape[1] // 4, 4, lat_h, lat_w)
+                msk = msk_pixel.transpose(1, 2).squeeze(0).to(dtype)  # 4 T H W
+            else:
+                msk = torch.zeros(4, latent_frame_num, lat_h, lat_w, device=device, dtype=dtype)
+                msk[:, :1] = 1
             y = torch.cat([msk, y]) # 4+C T H W
             mm.soft_empty_cache()
         else:
@@ -258,11 +335,12 @@ def multitalk_loop(self, **kwargs):
         latent = noise
 
         # injecting motion frames
-        if not is_first_clip and mode == "multitalk":
+        if not is_first_clip and mode != "infinitetalk":
             latent_motion_frames = latent_motion_frames.to(latent.dtype).to(device)
             motion_add_noise = torch.randn(latent_motion_frames.shape, device=torch.device("cpu"), generator=seed_g).to(device).contiguous()
             add_latent = add_noise(latent_motion_frames, motion_add_noise, timesteps[0])
             latent[:, :add_latent.shape[1]] = add_latent
+            del motion_add_noise, add_latent
 
         if offloaded:
             # Load weights
@@ -370,12 +448,13 @@ def multitalk_loop(self, **kwargs):
                     latent = image_latent * mask + latent * (1-mask)
 
             # injecting motion frames
-            if not is_first_clip and mode == "multitalk":
+            if not is_first_clip and mode != "infinitetalk":
                 latent_motion_frames = latent_motion_frames.to(latent.dtype).to(device)
                 motion_add_noise = torch.randn(latent_motion_frames.shape, device=torch.device("cpu"), generator=seed_g).to(device).contiguous()
                 add_latent = add_noise(latent_motion_frames, motion_add_noise, timesteps[i+1])
                 latent[:, :add_latent.shape[1]] = add_latent
-            else:
+                del motion_add_noise, add_latent
+            elif mode == "infinitetalk":
                 if humo_image_cond is None or not is_first_clip:
                     latent[:, :cur_motion_frames_latent_num] = latent_motion_frames
 
@@ -385,27 +464,34 @@ def multitalk_loop(self, **kwargs):
             offloaded = True
         if humo_image_cond is not None and humo_reference_count > 0:
             latent = latent[:,:-humo_reference_count]
+
         vae.to(device)
         videos = vae.decode(latent.unsqueeze(0).to(device, vae.dtype), device=device, tiled=tiled_vae, pbar=False)[0].cpu()
-
         vae.to(offload_device)
 
         sampling_pbar.close()
 
+        # crop drop_frames from end if enabled
+        if mode == "skyreelsv3" and drop_frames > 0 and not arrive_last_frame:
+            videos = videos[:, :-drop_frames]
+
         # optional color correction (less relevant for InfiniteTalk)
         if colormatch != "disabled":
-            videos = videos.permute(1, 2, 3, 0).float().numpy()
-            from color_matcher import ColorMatcher
-            cm = ColorMatcher()
-            cm_result_list = []
-            for img in videos:
-                if mode == "multitalk":
-                    cm_result = cm.transfer(src=img, ref=original_images[0].permute(1, 2, 3, 0).squeeze(0).cpu().float().numpy(), method=colormatch)
-                else:
-                    cm_result = cm.transfer(src=img, ref=cond_image[0].permute(1, 2, 3, 0).squeeze(0).cpu().float().numpy(), method=colormatch)
-                cm_result_list.append(torch.from_numpy(cm_result).to(vae.dtype))
+            if colormatch == "reinhard_torch":
+                videos = match_and_blend_colors(videos, original_color_reference, 1.0)
+            else:
+                videos = videos.permute(1, 2, 3, 0).float().numpy()
+                from color_matcher import ColorMatcher
+                cm = ColorMatcher()
+                cm_result_list = []
+                for img in videos:
+                    if mode == "infinitetalk":
+                        cm_result = cm.transfer(src=img, ref=cond_image[0].permute(1, 2, 3, 0).squeeze(0).cpu().float().numpy(), method=colormatch)
+                    else:
+                        cm_result = cm.transfer(src=img, ref=original_images[0].permute(1, 2, 3, 0).squeeze(0).cpu().float().numpy(), method=colormatch)
+                    cm_result_list.append(torch.from_numpy(cm_result).to(vae.dtype))
 
-            videos = torch.stack(cm_result_list, dim=0).permute(3, 0, 1, 2)
+                videos = torch.stack(cm_result_list, dim=0).permute(3, 0, 1, 2)
 
         # optionally save generated samples to disk
         if output_path:
@@ -441,7 +527,7 @@ def multitalk_loop(self, **kwargs):
 
         # Repeat audio emb
         if multitalk_embeds is not None:
-            audio_start_idx += (frame_num - cur_motion_frames_num - humo_reference_count)
+            audio_start_idx += (frame_num - cur_motion_frames_num - humo_reference_count - drop_frames)
             audio_end_idx = audio_start_idx + clip_length
             if audio_end_idx >= len(audio_embedding[0]):
                 arrive_last_frame = True

multitalk/nodes.py

@@ -461,13 +461,100 @@ class WanVideoImageToVideoMultiTalk:
         }
 
         return (image_embeds, output_path)
-    
+
+class WanVideoImageToVideoSkyreelsv3_audio:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "vae": ("WANVAE",),
+            "width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Width of the generation"}),
+            "height": ("INT", {"default": 480, "min": 64, "max": 29048, "step": 8, "tooltip": "Height of the generation"}),
+            "frame_window_size": ("INT", {"default": 81, "min": 1, "max": 10000, "step": 4, "tooltip": "The number of frames to process at once, should be a value the model is generally good at."}),
+            "motion_frame": ("INT", {"default": 5, "min": 1, "max": 10000, "step": 1, "tooltip": "Driven frame length used in the long video generation. Basically the overlap length."}),
+            "drop_frames": ("INT", {"default": 12, "min": 0, "max": 10000, "step": 1, "tooltip": "Additional frames to drop when advancing the audio window. Higher values = less overlap = faster generation but potentially less smooth transitions."}),
+            "tiled_vae": ("BOOLEAN", {"default": False, "tooltip": "Use tiled VAE encoding for reduced memory use"}),
+            "force_offload": ("BOOLEAN", {"default": False, "tooltip": "Whether to force offload the model within the loop for VAE operations, enable if you encounter memory issues."}),
+            "colormatch": (
+            [
+                'disabled',
+                'reinhard_torch',
+                'mkl',
+                'hm',
+                'reinhard',
+                'mvgd',
+                'hm-mvgd-hm',
+                'hm-mkl-hm',
+            ], {
+               "default": 'disabled', "tooltip": "Color matching method to use between the windows"
+            },),
+            },
+            "optional": {
+                "start_image": ("IMAGE", {"tooltip": "Images to encode"}),
+                "reference_video": ("IMAGE", {"tooltip": "Optional: Pre-generated reference video to use for keyframes instead of extracting from first generation. Should be color-matched to source image."}),
+                "clip_embeds": ("WANVIDIMAGE_CLIPEMBEDS", {"tooltip": "Clip vision encoded image"}),
+                "output_path": ("STRING", {"default": "", "tooltip": "If set, will save each window's resulting frames to this folder, also DISABLES returning the final video tensor to save memory"}),
+            }
+        }
+
+    RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", "STRING",)
+    RETURN_NAMES = ("image_embeds", "output_path")
+    FUNCTION = "process"
+    CATEGORY = "WanVideoWrapper"
+    DESCRIPTION = "Enables Multi/InfiniteTalk long video generation sampling method, the video is created in windows with overlapping frames. Not compatible or necessary to be used with context windows and many other features besides Multi/InfiniteTalk."
+
+    def process(self, vae, width, height, frame_window_size, motion_frame, drop_frames, force_offload, colormatch, start_image=None,
+                tiled_vae=False, clip_embeds=None, mode="multitalk", output_path="", reference_video=None):
+
+        H, W = height, width
+        num_frames = ((frame_window_size - 1) // 4) * 4 + 1
+
+        # Resize and rearrange the input image dimensions
+        if start_image is not None:
+            resized_start_image = common_upscale(start_image.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(0, 1)
+            resized_start_image = resized_start_image * 2 - 1
+            resized_start_image = resized_start_image.unsqueeze(0)
+
+        target_shape = (16, (num_frames - 1) // 4 + 1, height // 8, width // 8)
+
+        if output_path:
+            timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+            output_path = os.path.join(output_path, f"{timestamp}_{mode}_output")
+            os.makedirs(output_path, exist_ok=True)
+
+        processed_reference_video = None
+        if reference_video is not None:
+            processed_reference_video = common_upscale(reference_video.movedim(-1, 1), W, H, "lanczos", "disabled").movedim(0, 1)
+            processed_reference_video = processed_reference_video * 2 - 1
+
+        image_embeds = {
+            "multitalk_sampling": True,
+            "multitalk_start_image": resized_start_image if start_image is not None else None,
+            "frame_window_size": num_frames,
+            "motion_frame": motion_frame,
+            "drop_frames": drop_frames,
+            "use_pseudo_frames": True,
+            "reference_video": processed_reference_video,
+            "target_h": H,
+            "target_w": W,
+            "tiled_vae": tiled_vae,
+            "force_offload": force_offload,
+            "vae": vae,
+            "target_shape": target_shape,
+            "clip_context": clip_embeds.get("clip_embeds", None) if clip_embeds is not None else None,
+            "colormatch": colormatch,
+            "multitalk_mode": "skyreelsv3",
+            "output_path": output_path
+        }
+
+        return (image_embeds, output_path)
+
 NODE_CLASS_MAPPINGS = {
     "MultiTalkModelLoader": MultiTalkModelLoader,
     "MultiTalkWav2VecEmbeds": MultiTalkWav2VecEmbeds,
     "WanVideoImageToVideoMultiTalk": WanVideoImageToVideoMultiTalk,
     "Wav2VecModelLoader": Wav2VecModelLoader,
     "MultiTalkSilentEmbeds": MultiTalkSilentEmbeds,
+    "WanVideoImageToVideoSkyreelsv3_audio": WanVideoImageToVideoSkyreelsv3_audio,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -476,4 +563,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "WanVideoImageToVideoMultiTalk": "WanVideo Long I2V Multi/InfiniteTalk",
     "Wav2VecModelLoader": "Wav2vec2 Model Loader",
     "MultiTalkSilentEmbeds": "MultiTalk Silent Embeds",
+    "WanVideoImageToVideoSkyreelsv3_audio": "WanVideo Long SkyReelsV3 A2V",
 }

nodes.py

@@ -2,11 +2,13 @@ import os, gc, math
 import torch
 import torch.nn.functional as F
 import hashlib
+from tqdm import tqdm
 
 from .utils import(log, clip_encode_image_tiled, add_noise_to_reference_video, set_module_tensor_to_device)
 from .taehv import TAEHV
 
 from comfy import model_management as mm
+from comfy_api.latest import io
 from comfy.utils import ProgressBar, common_upscale
 from comfy.clip_vision import clip_preprocess, ClipVisionModel
 import folder_paths
@@ -366,10 +368,18 @@ class WanVideoTextEncode:
             cast_dtype = encoder.dtype
 
         params_to_keep = {'norm', 'pos_embedding', 'token_embedding'}
-        for name, param in encoder.model.named_parameters():
+        if hasattr(encoder, 'state_dict'):
+            model_state_dict = encoder.state_dict
+        else:
+            model_state_dict = encoder.model.state_dict()
+
+        params_list = list(encoder.model.named_parameters())
+        pbar = tqdm(params_list, desc="Loading T5 parameters", leave=True)
+        for name, param in pbar:
             dtype_to_use = dtype if any(keyword in name for keyword in params_to_keep) else cast_dtype
-            value = encoder.state_dict[name] if hasattr(encoder, 'state_dict') else encoder.model.state_dict()[name]
+            value = model_state_dict[name]
             set_module_tensor_to_device(encoder.model, name, device=device_to, dtype=dtype_to_use, value=value)
+        del model_state_dict
         if hasattr(encoder, 'state_dict'):
             del encoder.state_dict
             mm.soft_empty_cache()
@@ -550,6 +560,9 @@ class WanVideoApplyNAG:
             "nag_tau": ("FLOAT", {"default": 2.5, "min": 0.0, "max": 10.0, "step": 0.1}),
             "nag_alpha": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}),
             },
+            "optional": {
+                "inplace": ("BOOLEAN", {"default": True, "tooltip": "If true, modifies tensors in place to save memory. Leads to different numerical results which may change the output slightly."}),
+            }
         }
 
     RETURN_TYPES = ("WANVIDEOTEXTEMBEDS", )
@@ -558,7 +571,7 @@ class WanVideoApplyNAG:
     CATEGORY = "WanVideoWrapper"
     DESCRIPTION = "Adds NAG prompt embeds to original prompt embeds: 'https://github.com/ChenDarYen/Normalized-Attention-Guidance'"
 
-    def process(self, original_text_embeds, nag_text_embeds, nag_scale, nag_tau, nag_alpha):
+    def process(self, original_text_embeds, nag_text_embeds, nag_scale, nag_tau, nag_alpha, inplace=True):
         prompt_embeds_dict_copy = original_text_embeds.copy()
         prompt_embeds_dict_copy.update({
                 "nag_prompt_embeds": nag_text_embeds["prompt_embeds"],
@@ -566,6 +579,7 @@ class WanVideoApplyNAG:
                     "nag_scale": nag_scale,
                     "nag_tau": nag_tau,
                     "nag_alpha": nag_alpha,
+                    "inplace": inplace,
                 }
             })
         return (prompt_embeds_dict_copy,)
@@ -895,6 +909,8 @@ class WanVideoAddStoryMemLatents:
                     "vae": ("WANVAE",),
                     "embeds": ("WANVIDIMAGE_EMBEDS",),
                     "memory_images": ("IMAGE",),
+                    "rope_negative_offset": ("BOOLEAN", {"default": False, "tooltip": "Use positive RoPE frequency offset for the memory latents"}),
+                    "rope_negative_offset_frames": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1, "tooltip": "RoPE frequency offset for the memory latents"}),
                 }
         }
 
@@ -903,10 +919,11 @@ class WanVideoAddStoryMemLatents:
     FUNCTION = "add"
     CATEGORY = "WanVideoWrapper"
 
-    def add(self, vae, embeds, memory_images):
+    def add(self, vae, embeds, memory_images, rope_negative_offset, rope_negative_offset_frames):
         updated = dict(embeds)
         story_mem_latents, = WanVideoEncodeLatentBatch().encode(vae, memory_images)
         updated["story_mem_latents"] = story_mem_latents["samples"].squeeze(2).permute(1, 0, 2, 3)  # [C, T, H, W]
+        updated["rope_negative_offset_frames"] = rope_negative_offset_frames if rope_negative_offset else 0
         return (updated,)
 
 
@@ -2053,6 +2070,76 @@ class WanVideoAddTTMLatents:
 
         return (updated,)
 
+#region self-refine-video
+class WanVideoSelfRefineVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        # Default values for each range
+        default_ranges = [
+            (2, 5, 3),   # Range 1
+            (6, 14, 1),  # Range 2
+            (6, 14, 1),   # Range 3
+            (6, 14, 1),   # Range 4
+            (6, 14, 1),   # Range 5
+        ]
+
+        options = []
+        for num_ranges in range(1, 6):  # 1 to 5 ranges
+            range_inputs = []
+            for i in range(1, num_ranges + 1):
+                start_default, end_default, steps_default = default_ranges[i - 1]
+                range_inputs.extend([
+                    io.Int.Input(f"start_step{i}", default=start_default, min=0, max=999, step=1, tooltip=f"Start step for range {i}"),
+                    io.Int.Input(f"end_step{i}", default=end_default, min=0, max=999, step=1, tooltip=f"End step for range {i}"),
+                    io.Int.Input(f"steps_{i}", default=steps_default, min=1, max=100, step=1, tooltip=f"Number of P&P steps for range {i}"),
+                ])
+            options.append(io.DynamicCombo.Option(
+                key=str(num_ranges),
+                inputs=range_inputs
+            ))
+
+        return io.Schema(
+            node_id="WanVideoSelfRefineVideo",
+            category="WanVideoWrapper",
+            description="https://github.com/agwmon/self-refine-video - Configure stochastic plan for Perturb-and-Project sampling",
+            inputs=[
+                io.Custom("WANVIDIMAGE_EMBEDS").Input("embeds", tooltip="Image embeddings to update"),
+                io.Float.Input(
+                    "uncertainty_threshold",
+                    default=0.25, min=0.0, max=1.0, step=0.01,
+                    tooltip="Lower values make it harder for regions to be considered \"certain\", meaning more pixels will continue being refined. Higher values make it easier to lock in pixels early."
+                ),
+                io.Float.Input("certain_percentage", default=0.999, min=0.0, max=1.0, step=0.001, tooltip="Higher values = stricter requirement = fewer early stops = more iterations"),
+                io.DynamicCombo.Input("num_ranges", options=options, display_name="Number of Ranges", tooltip="Number of step ranges to configure for the stochastic plan"),
+            ],
+            outputs=[
+                io.Custom("WANVIDIMAGE_EMBEDS").Output(display_name="image_embeds", tooltip="Updated image embeddings with self-refine parameters"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, embeds, uncertainty_threshold, certain_percentage, num_ranges) -> io.NodeOutput:
+        updated = dict(embeds)
+        updated["self_refine_uncertainty_threshold"] = uncertainty_threshold
+        updated["self_refine_certain_percentage"] = certain_percentage
+
+        # Build stochastic plan from the dynamic inputs in list format: [(start, end, steps), ...]
+        stochastic_plan = []
+        range_keys = sorted([k for k in num_ranges.keys() if k.startswith('start_step')])
+
+        for start_key in range_keys:
+            i = start_key.replace('start_step', '')
+            start = num_ranges.get(f"start_step{i}")
+            end = num_ranges.get(f"end_step{i}")
+            steps = num_ranges.get(f"steps_{i}")
+
+            if start is not None and end is not None and steps is not None:
+                stochastic_plan.append((start, end, steps))
+
+        updated["stochastic_plan"] = stochastic_plan
+
+        return io.NodeOutput(updated)
+
 #region VideoDecode
 class WanVideoDecode:
     @classmethod
@@ -2307,6 +2394,7 @@ NODE_CLASS_MAPPINGS = {
     "WanVideoAddTTMLatents": WanVideoAddTTMLatents,
     "WanVideoAddStoryMemLatents": WanVideoAddStoryMemLatents,
     "WanVideoSVIProEmbeds": WanVideoSVIProEmbeds,
+    "WanVideoSelfRefineVideo": WanVideoSelfRefineVideo,
     }
 
 NODE_DISPLAY_NAME_MAPPINGS = {

nodes_model_loading.py

@@ -52,17 +52,6 @@ def update_folder_names_and_paths(key, targets=[]):
         log.warning(f"Unknown file list already present on key {key}: {base}")
 update_folder_names_and_paths("unet_gguf", ["diffusion_models", "unet"])
 
-class WanVideoModel(comfy.model_base.BaseModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.pipeline = {}
-
-    def __getitem__(self, k):
-        return self.pipeline[k]
-
-    def __setitem__(self, k, v):
-        self.pipeline[k] = v
-
 try:
     from comfy.latent_formats import Wan21, Wan22
     latent_format = Wan21
@@ -71,16 +60,27 @@ except: #for backwards compatibility
     from comfy.latent_formats import HunyuanVideo
     latent_format = HunyuanVideo
 
+class WanVideoModel(torch.nn.Module):
+    def __init__(self, model_config, transformer, device=None):
+        super().__init__()
+        self.latent_format = model_config.latent_format
+        self.model_config = model_config
+        self.device = device
+        self.current_patcher = None
+        self.diffusion_model = transformer
+        self.pipeline = {}
+
+    def __getitem__(self, k):
+        return self.pipeline[k]
+
+    def __setitem__(self, k, v):
+        self.pipeline[k] = v
+
 class WanVideoModelConfig:
-    def __init__(self, dtype, latent_format=latent_format):
+    def __init__(self, latent_format=latent_format):
         self.unet_config = {}
         self.unet_extra_config = {}
         self.latent_format = latent_format
-        #self.latent_format.latent_channels = 16
-        self.manual_cast_dtype = dtype
-        self.sampling_settings = {"multiplier": 1.0}
-        self.memory_usage_factor = 2.0
-        self.unet_config["disable_unet_model_creation"] = True
 
 def filter_state_dict_by_blocks(state_dict, blocks_mapping, layer_filter=[]):
     filtered_dict = {}
@@ -810,7 +810,6 @@ def load_weights(transformer, sd=None, weight_dtype=None, base_dtype=None,
                       "adapter", "add", "ref_conv", "casual_audio_encoder", "cond_encoder", "frame_packer", "audio_proj_glob", "face_encoder", "fuser_block"}
     param_count = sum(1 for _ in transformer.named_parameters())
     pbar = ProgressBar(param_count)
-    cnt = 0
     block_idx = vace_block_idx = None
 
     if gguf:
@@ -830,7 +829,7 @@ def load_weights(transformer, sd=None, weight_dtype=None, base_dtype=None,
             all_tensors.extend(r.tensors)
         for tensor in all_tensors:
             name = rename_fuser_block(tensor.name)
-            if "glob" not in name and "audio_proj" in name:
+            if "glob" not in name and "multitalk_audio_proj" not in name and "audio_proj" in name:
                 name = name.replace("audio_proj", "multitalk_audio_proj")
             load_device = device
             if "vace_blocks." in name:
@@ -920,9 +919,7 @@ def load_weights(transformer, sd=None, weight_dtype=None, base_dtype=None,
                     load_device = offload_device
         # Set tensor to device
         set_module_tensor_to_device(transformer, name, device=load_device, dtype=dtype_to_use, value=value)
-        cnt += 1
-        if cnt % 100 == 0:
-            pbar.update(100)
+        pbar.update(1)
 
     #[print(name, param.device, param.dtype) for name, param in transformer.named_parameters()]
     memory_on_device = get_module_memory_mb_per_device(transformer)
@@ -931,6 +928,8 @@ def load_weights(transformer, sd=None, weight_dtype=None, base_dtype=None,
     for dev, mem_mb in memory_on_device.items():
         log.info(f"Device: {dev:8s} | Memory: {mem_mb:,.2f} MB")
 
+    if hasattr(pbar, "_last_sent_value"):
+        pbar._last_sent_value = -1
     pbar.update_absolute(0)
 
 def patch_control_lora(transformer, device):
@@ -1512,7 +1511,45 @@ class WanVideoModelLoader:
                     block.cross_attn.ip_adapter_single_stream_k_proj = nn.Linear(context_dim, dim, bias=False)
                     block.cross_attn.ip_adapter_single_stream_v_proj = nn.Linear(context_dim, dim, bias=False)
 
-        if multitalk_model is not None:
+        # LongCat Avatar
+        if "multitalk_audio_proj.proj1.weight" in sd and "blocks.0.audio_cross_attn.q_norm.weight" in sd:
+            log.info("MultiTalk/InfiniteTalk model detected, patching model...")
+            from .multitalk.multitalk import AudioProjModel
+            from .wanvideo.modules.model import WanLayerNorm
+            from .LongCat.layers import SingleStreamAttention
+
+
+            for block in transformer.blocks:
+                with init_empty_weights():
+                    if "blocks.0.audio_modulation.1.weight" in sd:
+                        block.audio_modulation = nn.Sequential(nn.SiLU(), nn.Linear(512, 3 * dim, bias=True))
+                    block.norm_x = WanLayerNorm(dim, transformer.eps, elementwise_affine=True)
+                    block.audio_cross_attn = SingleStreamAttention(
+                            dim=dim,
+                            encoder_hidden_states_dim=768,
+                            num_heads=num_heads,
+                        qkv_bias=True,
+                        qk_norm=True,
+                        class_range=24,
+                        class_interval=4,
+                        attention_mode=attention_mode,
+                    )
+                    multitalk_proj_model = AudioProjModel()
+            transformer.multitalk_audio_proj = multitalk_proj_model
+        # SkyreelsV3
+        elif "blocks.1.audio_cross_attn.kv_linear.weight" in sd and "audio_proj.proj1.weight" in sd:
+            sd = {k.replace("audio_proj", "multitalk_audio_proj"): v for k, v in sd.items()}
+            # init audio module
+            from .multitalk.multitalk import SingleStreamMultiAttention, AudioProjModel
+            from .wanvideo.modules.model import WanLayerNorm
+
+            for block in transformer.blocks:
+                with init_empty_weights():
+                    block.norm_x = WanLayerNorm(dim, transformer.eps, elementwise_affine=True)
+                    block.audio_cross_attn = SingleStreamMultiAttention(dim=dim, num_heads=num_heads, attention_mode=attention_mode)
+
+            transformer.multitalk_audio_proj = AudioProjModel()
+        elif multitalk_model is not None:
             multitalk_model_type = multitalk_model.get("model_type", "MultiTalk")
             log.info(f"{multitalk_model_type} detected, patching model...")
 
@@ -1529,15 +1566,7 @@ class WanVideoModelLoader:
             for block in transformer.blocks:
                 with init_empty_weights():
                     block.norm_x = WanLayerNorm(dim, transformer.eps, elementwise_affine=True)
-                    block.audio_cross_attn = SingleStreamMultiAttention(
-                            dim=dim,
-                            encoder_hidden_states_dim=768,
-                            num_heads=num_heads,
-                        qkv_bias=True,
-                        class_range=24,
-                        class_interval=4,
-                        attention_mode=attention_mode,
-                    )
+                    block.audio_cross_attn = SingleStreamMultiAttention(dim=dim, num_heads=num_heads, attention_mode=attention_mode)
             transformer.multitalk_audio_proj = multitalk_model["proj_model"]
             transformer.multitalk_model_type = multitalk_model_type
 
@@ -1555,39 +1584,6 @@ class WanVideoModelLoader:
 
             sd.update(extra_sd)
             del extra_sd
-        elif "multitalk_audio_proj.proj1.weight" in sd:
-            log.info("MultiTalk/InfiniteTalk model detected, patching model...")
-            from .multitalk.multitalk import AudioProjModel
-            from .wanvideo.modules.model import WanLayerNorm
-            from .LongCat.layers import SingleStreamAttention
-
-            audio_window = 5
-            vae_scale = 4
-
-            for block in transformer.blocks:
-                with init_empty_weights():
-                    if "blocks.0.audio_modulation.1.weight" in sd:
-                        block.audio_modulation = nn.Sequential(nn.SiLU(), nn.Linear(512, 3 * dim, bias=True))
-                    block.norm_x = WanLayerNorm(dim, transformer.eps, elementwise_affine=True)
-                    block.audio_cross_attn = SingleStreamAttention(
-                            dim=dim,
-                            encoder_hidden_states_dim=768,
-                            num_heads=num_heads,
-                        qkv_bias=True,
-                        qk_norm=True,
-                        class_range=24,
-                        class_interval=4,
-                        attention_mode=attention_mode,
-                    )
-                    multitalk_proj_model = AudioProjModel(
-                            seq_len=audio_window,
-                            seq_len_vf=audio_window+vae_scale-1,
-                            intermediate_dim=512,
-                            output_dim=768,
-                            context_tokens=32,
-                            norm_output_audio=True,
-                    )
-            transformer.multitalk_audio_proj = multitalk_proj_model
 
         sd = {k.replace(".weight_scale", ".scale_weight"): v for k, v in sd.items()}
 
@@ -1613,11 +1609,7 @@ class WanVideoModelLoader:
             transformer.text_projection = nn.Sequential(nn.Linear(sd["text_projection.0.weight"].shape[1], text_dim), nn.GELU(approximate='tanh'), nn.Linear(text_dim, text_dim))
 
         latent_format=Wan22 if dim == 3072 else Wan21
-        comfy_model = WanVideoModel(
-            WanVideoModelConfig(base_dtype, latent_format=latent_format),
-            model_type=comfy.model_base.ModelType.FLOW,
-            device=device,
-        )
+        comfy_model = WanVideoModel(WanVideoModelConfig(latent_format=latent_format), device=device, transformer=transformer)
 
         # SteadyDancer
         if "condition_embedding_align.cross_attn.in_proj_bias" in sd:
@@ -1681,6 +1673,24 @@ class WanVideoModelLoader:
                     block.ref_attn_v_img = nn.Linear(in_features, out_features)
                     block.ref_attn_norm_k_img = WanRMSNorm(out_features, eps=1e-6)
 
+        if "blocks.0.control_blocks_dense.cross_attn.k.weight" in sd:
+            log.info("LongVie2 model detected, patching model...")
+            from .LongVie2.modules import WanModelDualControl
+            control_layers = 12
+            with init_empty_weights():
+                dual_controller = WanModelDualControl(dim=5120, ffn_dim=13824, eps=1e-06, num_heads=40, control_layers=control_layers)
+                for b in range(control_layers):
+                    transformer.blocks[b].control_blocks_dense = dual_controller.control_blocks_dense[b]
+                    transformer.blocks[b].control_blocks_sparse = dual_controller.control_blocks_sparse[b]
+                    transformer.blocks[b].control_combine_linears = dual_controller.control_combine_linears[b]
+                transformer.dual_controller = nn.Module()
+                transformer.dual_controller.control_initial_combine_linear_dense = dual_controller.control_initial_combine_linear_dense
+                transformer.dual_controller.control_initial_combine_linear_sparse = dual_controller.control_initial_combine_linear_sparse
+                transformer.dual_controller.control_t_mod = dual_controller.control_t_mod
+                transformer.dual_controller.control_text_linear = dual_controller.control_text_linear
+                transformer.dual_controller_freqs = dual_controller.freqs
+
+
         comfy_model.diffusion_model = transformer
         comfy_model.load_device = transformer_load_device
         patcher = comfy.model_patcher.ModelPatcher(comfy_model, device, offload_device)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "ComfyUI-WanVideoWrapper"
 description = "ComfyUI wrapper nodes for WanVideo"
-version = "1.4.5"
+version = "1.4.7"
 license = {file = "LICENSE"}
 dependencies = ["accelerate >= 1.2.1", "diffusers >= 0.33.0", "peft >= 0.17.0", "ftfy", "gguf >= 0.17.1", "pyloudnorm"]
 

utils.py

@@ -7,9 +7,14 @@ from pathlib import Path
 import gc
 import types, collections
 from comfy.utils import ProgressBar, copy_to_param, set_attr_param
-from comfy.model_patcher import get_key_weight, string_to_seed
+from comfy.model_patcher import get_key_weight
 from comfy.lora import calculate_weight
 
+try:
+    from comfy.utils import string_to_seed
+except:
+    from comfy.model_patcher import string_to_seed
+
 from comfy.float import stochastic_rounding
 from .custom_linear import remove_lora_from_module
 import folder_paths
@@ -190,9 +195,9 @@ def set_module_tensor_to_device(module, tensor_name, device, value=None, dtype=N
             device = device_quantization
         if is_buffer:
             module._buffers[tensor_name] = new_value
-        elif value is not None or not check_device_same(torch.device(device), module._parameters[tensor_name].device):
+        elif value is not None or not check_device_same(device, module._parameters[tensor_name].device):
             param_cls = type(module._parameters[tensor_name])
-            new_value = param_cls(new_value, requires_grad=False).to(device)
+            new_value = param_cls(new_value, requires_grad=False)
             module._parameters[tensor_name] = new_value
 
     #if device != "cpu":
@@ -718,3 +723,55 @@ def temporal_score_rescaling(model_output, sample, timestep, k=1.0, tsr_sigma=0.
     if not t == 1.0:
         model_output = (ratio * ((1-t) * model_output + sample) - sample) / (1 - t)
     return model_output
+
+def match_and_blend_colors(
+    source_chunk: torch.Tensor,  # (C, T, H, W), range [-1, 1]
+    reference_image: torch.Tensor,  # (C, 1, H, W), range [-1, 1]
+    strength: float,
+) -> torch.Tensor:
+    import kornia
+    if strength == 0.0:
+        return source_chunk
+    source_chunk = source_chunk.unsqueeze(0)  # (1, C, T, H, W)
+
+    # shapes
+    B, C, T, H, W = source_chunk.shape
+    input_dtype = source_chunk.dtype
+
+    # [-1,1] -> [0,1]
+    src_01 = (source_chunk + 1.0) * 0.5
+    ref_01 = (reference_image + 1.0) * 0.5
+
+    src32 = src_01.to(torch.float32)
+    ref32 = ref_01.to(torch.float32)
+
+    # (B, C, T, H, W) -> (B*T, C, H, W)
+    src_bt = src32.permute(0, 2, 1, 3, 4).contiguous().view(B * T, C, H, W)
+    ref_bchw = ref32[:, :, 0, :, :].contiguous()
+
+    # RGB->Lab
+    src_lab = kornia.color.rgb_to_lab(src_bt)  # (B*T, C, H, W)
+    ref_lab = kornia.color.rgb_to_lab(ref_bchw)  # (B,   C, H, W)
+
+    src_lab_flat = src_lab.view(B * T, C, -1)  # (B*T, C, HW)
+    ref_lab_flat = ref_lab.view(B, C, -1)  # (B,   C, HW)
+    src_std, src_mean = torch.std_mean(src_lab_flat, dim=-1, keepdim=True, unbiased=False)
+    ref_std, ref_mean = torch.std_mean(ref_lab_flat, dim=-1, keepdim=True, unbiased=False)
+    src_std = src_std.clamp_min_(1e-6)
+
+    ref_mean_bt = ref_mean.repeat_interleave(T, dim=0)  # (B*T, C, 1)
+    ref_std_bt = ref_std.repeat_interleave(T, dim=0)  # (B*T, C, 1)
+
+    corrected_lab_flat = (src_lab_flat - src_mean) * (ref_std_bt / src_std) + ref_mean_bt
+    corrected_lab = corrected_lab_flat.view(B * T, C, H, W)
+
+    # Lab->RGB
+    corrected_rgb_01 = kornia.color.lab_to_rgb(corrected_lab)  # (B*T, C, H, W)
+
+    blended_rgb_01 = (1.0 - strength) * src_bt + strength * corrected_rgb_01
+
+    # (B, C, T, H, W)
+    blended_rgb_01 = blended_rgb_01.view(B, T, C, H, W).permute(0, 2, 1, 3, 4).contiguous()
+
+    # [0,1] -> [-1,1]
+    return (blended_rgb_01 * 2.0 - 1.0)[0].to(dtype=input_dtype)

wanvideo/modules/model.py

@@ -558,39 +558,53 @@ class WanSelfAttention(nn.Module):
         # output
         return self.o(x.flatten(2))
 
-    def normalized_attention_guidance(self, b, n, d, q, context, nag_context=None, nag_params={}):
+    def nag_attention(self, b, n, d, q, context, nag_context=None):
+        k_positive = self.norm_k(self.k(context).to(self.norm_k.weight.dtype)).view(b, -1, n, d).to(q.dtype)
+        v_positive = self.v(context).view(b, -1, n, d)
+        x_positive = attention(q, k_positive, v_positive, attention_mode=self.attention_mode, heads=self.num_heads)
+        del k_positive, v_positive
+
+        k_negative = self.norm_k(self.k(nag_context).to(self.norm_k.weight.dtype)).view(b, -1, n, d).to(q.dtype)
+        v_negative = self.v(nag_context).view(b, -1, n, d)
+        x_negative = attention(q, k_negative, v_negative, attention_mode=self.attention_mode, heads=self.num_heads)
+        del k_negative, v_negative
+
+        return x_positive.flatten(2), x_negative.flatten(2)
+
+    def normalized_attention_guidance(self, x_positive, x_negative,nag_params={}):
         # NAG text attention
-        context_positive = context
-        context_negative = nag_context
         nag_scale = nag_params['nag_scale']
         nag_alpha = nag_params['nag_alpha']
         nag_tau = nag_params['nag_tau']
+        inplace = nag_params.get('inplace', True)
 
-        k_positive = self.norm_k(self.k(context_positive).to(self.norm_k.weight.dtype)).view(b, -1, n, d).to(q.dtype)
-        v_positive = self.v(context_positive).view(b, -1, n, d)
-        k_negative = self.norm_k(self.k(context_negative).to(self.norm_k.weight.dtype)).view(b, -1, n, d).to(q.dtype)
-        v_negative = self.v(context_negative).view(b, -1, n, d)
+        if inplace:
+            nag_guidance = x_negative.mul_(nag_scale - 1).neg_().add_(x_positive, alpha=nag_scale)
+        else:
+            nag_guidance = x_positive * nag_scale - x_negative * (nag_scale - 1)
+        del x_negative
 
-        x_positive = attention(q, k_positive, v_positive, attention_mode=self.attention_mode, heads=self.num_heads)
-        x_positive = x_positive.flatten(2)
-
-        x_negative = attention(q, k_negative, v_negative, attention_mode=self.attention_mode, heads=self.num_heads)
-        x_negative = x_negative.flatten(2)
-
-        nag_guidance = x_positive * nag_scale - x_negative * (nag_scale - 1)
-        
         norm_positive = torch.norm(x_positive, p=1, dim=-1, keepdim=True)
         norm_guidance = torch.norm(nag_guidance, p=1, dim=-1, keepdim=True)
-        
+
         scale = norm_guidance / norm_positive
-        scale = torch.nan_to_num(scale, nan=10.0)
-        
+        torch.nan_to_num_(scale, nan=10.0)
         mask = scale > nag_tau
+        del scale
+
         adjustment = (norm_positive * nag_tau) / (norm_guidance + 1e-7)
-        nag_guidance = torch.where(mask, nag_guidance * adjustment, nag_guidance)
+        del norm_positive, norm_guidance
+
+        nag_guidance.mul_(torch.where(mask, adjustment, 1.0))
         del mask, adjustment
-        
-        return nag_guidance * nag_alpha + x_positive * (1 - nag_alpha)
+
+        if inplace:
+            nag_guidance.sub_(x_positive).mul_(nag_alpha).add_(x_positive)
+        else:
+            nag_guidance = nag_guidance * nag_alpha + x_positive * (1 - nag_alpha)
+        del x_positive
+
+        return nag_guidance
 
 class LoRALinearLayer(nn.Module):
     def __init__(
@@ -648,7 +662,10 @@ class WanT2VCrossAttention(WanSelfAttention):
             q = self.norm_q(self.q(x).to(self.norm_q.weight.dtype),num_chunks=2 if rope_func == "comfy_chunked" else 1).to(x.dtype).view(b, -1, n, d)
 
         if nag_context is not None:
-            x = self.normalized_attention_guidance(b, n, d, q, context, nag_context, nag_params)
+            x_positive, x_negative = self.nag_attention(b, n, d, q, context, nag_context)
+            del q
+            x = self.normalized_attention_guidance(x_positive, x_negative, nag_params)
+            del x_positive, x_negative
         else:
             if is_longcat:
                 k = self.norm_k(self.k(context).to(self.norm_k.weight.dtype).view(b, -1, n, d)).to(x.dtype)
@@ -752,7 +769,8 @@ class WanI2VCrossAttention(WanSelfAttention):
             k_img = self.norm_k_img(self.k_img(clip_embed).to(self.norm_k_img.weight.dtype)).view(b, -1, n, d).to(x.dtype)
             v_img = self.v_img(clip_embed).view(b, -1, n, d)
             img_x = attention(q, k_img, v_img, attention_mode=self.attention_mode, heads=self.num_heads).flatten(2)
-            x = x_text + img_x
+            x_text.add_(img_x)
+            x = x_text
         else:
             x = x_text
 
@@ -1280,7 +1298,7 @@ class WanAttentionBlock(nn.Module):
                     y[:, tr_end:] * gate_msa
                 ], dim=1).to(input_dtype)
             else:
-                x = x.addcmul(y, gate_msa)
+                x.addcmul_(y, gate_msa)
         del y, gate_msa
 
         # cross-attention & ffn function
@@ -1310,11 +1328,10 @@ class WanAttentionBlock(nn.Module):
                 x = self.split_cross_attn_ffn(x, context, shift_mlp, scale_mlp, gate_mlp, clip_embed, grid_sizes)
                 return x, x_ip, lynx_ref_feature, x_ovi
             else:
-                x = x + self.cross_attn(self.norm3(x.to(self.norm3.weight.dtype)).to(input_dtype), context, grid_sizes, clip_embed=clip_embed, audio_proj=audio_proj, audio_scale=audio_scale,
+                x += self.cross_attn(self.norm3(x.to(self.norm3.weight.dtype)).to(input_dtype), context, grid_sizes, clip_embed=clip_embed, audio_proj=audio_proj, audio_scale=audio_scale,
                                     num_latent_frames=num_latent_frames, nag_params=nag_params, nag_context=nag_context,
                                     rope_func=self.rope_func, inner_t=inner_t, inner_c=inner_c, cross_freqs=cross_freqs,
-                                    adapter_proj=adapter_proj, ip_scale=ip_scale, orig_seq_len=original_seq_len, lynx_x_ip=lynx_x_ip, lynx_ip_scale=lynx_ip_scale, longcat_num_cond_latents=longcat_num_cond_latents)
-                x = x.to(input_dtype)
+                                    adapter_proj=adapter_proj, ip_scale=ip_scale, orig_seq_len=original_seq_len, lynx_x_ip=lynx_x_ip, lynx_ip_scale=lynx_ip_scale, longcat_num_cond_latents=longcat_num_cond_latents).to(input_dtype)
                 # MultiTalk
                 if multitalk_audio_embedding is not None and not isinstance(self, VaceWanAttentionBlock):
 
@@ -1328,7 +1345,8 @@ class WanAttentionBlock(nn.Module):
                     else:
                         x_audio = self.audio_cross_attn(self.norm_x(x.to(self.norm_x.weight.dtype)).to(input_dtype), encoder_hidden_states=multitalk_audio_embedding,
                                                     shape=grid_sizes[0], x_ref_attn_map=x_ref_attn_map, human_num=human_num)
-                    x = x.add(x_audio, alpha=audio_scale)
+                    x.add_(x_audio, alpha=audio_scale)
+                    del x_audio
 
                 # MTV-Crafter Motion Attention
                 if self.use_motion_attn and mtv_motion_tokens is not None and mtv_motion_rotary_emb is not None:
@@ -2188,7 +2206,7 @@ class WanModel(torch.nn.Module):
 
     def rope_encode_comfy(self, t, h, w, freq_offset=0, t_start=0, ref_frame_shape=None, pose_frame_shape=None,
                           steps_t=None, steps_h=None, steps_w=None, ntk_alphas=[1,1,1], device=None, dtype=None,
-                          ref_frame_index=10, longcat_num_ref_latents=0):
+                          ref_frame_index=10, longcat_num_ref_latents=0, num_memory_frames=3, rope_negative_offset=5):
 
         patch_size = self.patch_size
         t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
@@ -2212,6 +2230,15 @@ class WanModel(torch.nn.Module):
                 torch.arange(0, steps_t - longcat_num_ref_latents, dtype=dtype, device=device)
             ], dim=0)
             img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + grid_t.reshape(-1, 1, 1)
+        elif num_memory_frames > 0 and rope_negative_offset > 0:
+            # Negative RoPE shift for memory frames
+            # Memory frames get negative indices: {-f_m*S, -(f_m-1)*S, ..., -S}
+            # Current video frames start from 0: {0, 1, ..., f-1}
+            memory_indices = torch.arange(-num_memory_frames * rope_negative_offset, 0, rope_negative_offset, dtype=dtype, device=device)
+            current_indices = torch.arange(0, steps_t - num_memory_frames, dtype=dtype, device=device)
+            grid_t = torch.cat([memory_indices, current_indices], dim=0)
+            log.info(f"{num_memory_frames} memory frames, temporal rope positions: {grid_t}")
+            img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + grid_t.reshape(-1, 1, 1)
         else:
             # Standard temporal encoding
             img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start+freq_offset, t_start+freq_offset + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1)
@@ -2319,7 +2346,10 @@ class WanModel(torch.nn.Module):
         sdancer_input=None,  # SteadyDancer
         one_to_all_input=None, one_to_all_controlnet_strength=0.0, # One-to-All
         scail_input=None,  # SCAIL pose
+        dual_control_input=None,  # LongVie2 dual controlnet
         transformer_options={},
+        rope_negative_offset=0,
+        num_memory_frames=0,
     ):
         r"""
         Forward pass through the diffusion model
@@ -2546,6 +2576,16 @@ class WanModel(torch.nn.Module):
         x = [u.flatten(2).transpose(1, 2) for u in x]
         self.original_seq_len = x[0].shape[1]
 
+        prev_latent = None
+        if dual_control_input is not None:
+            prev_latent = dual_control_input.get("prev_latent", None)
+            if prev_latent is not None:
+                F += prev_latent.shape[2]
+                prev_x = [self.original_patch_embedding(u.unsqueeze(0).to(torch.float32)).to(x[0].dtype) for u in prev_latent]
+                prev_x = [u.flatten(2).transpose(1, 2).to(self.base_dtype) for u in prev_x]
+                seq_len += prev_x[0].shape[1]
+                x = [torch.cat([u, v], dim=1) for u, v in zip(prev_x, x)]
+
         # SCAIL pose
         if scail_input is not None:
             scail_pose_latents = scail_input.get("pose_latent", None)
@@ -2554,6 +2594,7 @@ class WanModel(torch.nn.Module):
                 scail_x = [u.flatten(2).transpose(1, 2) * scail_input.get("pose_strength", 1) for u in scail_x]
                 x = [torch.cat([u, v], dim=1) for u, v in zip(x, scail_x)]
                 seq_len += scail_x[0].shape[1]
+                del scail_x
                 pose_frame_shape = scail_pose_latents.shape
 
         seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.int32)
@@ -2632,6 +2673,8 @@ class WanModel(torch.nn.Module):
                 self.rope_embedder.k,
                 tuple(ntk_alphas),
                 longcat_num_ref_latents,
+                rope_negative_offset,
+                num_memory_frames,
             )
 
             # Check cache using key comparison
@@ -2647,6 +2690,8 @@ class WanModel(torch.nn.Module):
                     ref_frame_shape=ref_frame_shape,
                     pose_frame_shape=pose_frame_shape,
                     longcat_num_ref_latents=longcat_num_ref_latents,
+                    rope_negative_offset=rope_negative_offset,
+                    num_memory_frames=num_memory_frames,
                     device=x.device,
                     dtype=x.dtype
                 )
@@ -2836,6 +2881,44 @@ class WanModel(torch.nn.Module):
             chunked_self_attention = False
             seq_chunks = 0
 
+        # dual control
+        if dual_control_input is not None and dual_control_input["start_percent"] <= current_step_percentage <= dual_control_input["end_percent"]:
+            dense_latent = dual_control_input["dense_input_latent"]
+            print("dense_latent shape:", dense_latent.shape)
+            sparse_latent = dual_control_input["sparse_input_latent"]
+            if dense_latent is None and sparse_latent is None:
+                raise ValueError("At least one of dense_input_latent or sparse_input_latent must be provided in dual_control_input")
+
+            if dense_latent is not None:
+                dense_x = [self.original_patch_embedding(u.unsqueeze(0).to(torch.float32)).to(x[0].dtype) for u in dense_latent]
+                dense_x = [u.flatten(2).transpose(1, 2).to(self.base_dtype) for u in dense_x]
+                dense = self.dual_controller.control_initial_combine_linear_dense(dense_x[0])
+
+            if sparse_latent is not None:
+                sparse_x = [self.original_patch_embedding(u.unsqueeze(0).to(torch.float32)).to(x[0].dtype) for u in sparse_latent]
+                sparse_x = [u.flatten(2).transpose(1, 2).to(self.base_dtype) for u in sparse_x]
+                sparse = self.dual_controller.control_initial_combine_linear_sparse(sparse_x[0])
+
+            if dense_latent is None:
+                dense = torch.zeros_like(sparse)
+            elif sparse_latent is None:
+                sparse = torch.zeros_like(dense)
+
+            control_context = clip_fea_control = None
+            if context != []:
+                control_context = self.dual_controller.control_text_linear(context)
+                if clip_embed is not None:
+                    clip_fea_control = self.dual_controller.control_text_linear(clip_embed)
+            control_t_mod = self.dual_controller.control_t_mod(e0)
+
+            control_freqs = torch.cat([
+                self.dual_controller_freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
+                self.dual_controller_freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
+                self.dual_controller_freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
+            ], dim=-1).reshape(f * h * w, 1, -1).to(x.device)
+        else:
+            dual_control_input = None
+
         # MultiTalk
         if multitalk_audio is not None:
             self.multitalk_audio_proj.to(self.main_device)
@@ -3191,6 +3274,18 @@ class WanModel(torch.nn.Module):
                 x, x_ip, lynx_ref_feature, x_ovi = block(x, x_ip=x_ip, lynx_ref_feature=lynx_ref_feature, x_ovi=x_ovi, x_onetoall_ref=x_onetoall_ref, onetoall_freqs=onetoall_freqs, attention_mode_override=attention_mode, **kwargs)
                 # ---post block----#
 
+                # dual controlnet
+                if dual_control_input is not None and (hasattr(block, "control_blocks_dense") or hasattr(block, "control_blocks_sparse")):
+                    if dense_latent is not None and hasattr(block, "control_blocks_dense"):
+                        dense = block.control_blocks_dense(dense, control_context, control_t_mod, control_freqs, clip_fea=clip_fea_control)
+                    if sparse_latent is not None and hasattr(block, "control_blocks_sparse"):
+                        sparse = block.control_blocks_sparse(sparse, control_context, control_t_mod, control_freqs, clip_fea=clip_fea_control)
+
+                    if prev_latent is not None:
+                        x[:, -self.original_seq_len:] += block.control_combine_linears(dense + sparse) * dual_control_input["strength"]
+                    else:
+                        x += block.control_combine_linears(dense + sparse) * dual_control_input["strength"]
+
                 if self.audio_injector is not None and s2v_audio_input is not None:
                     x = self.audio_injector_forward(b, x, merged_audio_emb, scale=s2v_audio_scale) #s2v
                 if block.has_face_fuser_block and motion_vec is not None:
@@ -3293,8 +3388,10 @@ class WanModel(torch.nn.Module):
         #    x = x[:, :self.original_seq_len]
             #grid_sizes = torch.stack([torch.tensor([u[0] - 1, u[1], u[2]]) for u in grid_sizes]).to(grid_sizes.device)
 
-
-        x = x[:, :self.original_seq_len]
+        if prev_latent is not None:
+            x = x[:, -self.original_seq_len:]
+        else:
+            x = x[:, :self.original_seq_len]
 
         x = self.head(x, e.to(x.device), temp_length=F,
                       e_tr=e_token_replace.to(x.device) if use_token_replace else None, tr_start=token_replace_start, tr_num=replace_token_num)