Merge branch 'master' into worksplit-multigpu

README.md

@@ -55,7 +55,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
 - Image Models
-   - SD1.x, SD2.x,
+   - SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/))
    - [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
    - [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/)
    - [SD3 and SD3.5](https://comfyanonymous.github.io/ComfyUI_examples/sd3/)
@@ -77,6 +77,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
    - [Nvidia Cosmos](https://comfyanonymous.github.io/ComfyUI_examples/cosmos/) and [Cosmos Predict2](https://comfyanonymous.github.io/ComfyUI_examples/cosmos_predict2/)
    - [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
+   - [Wan 2.2](https://comfyanonymous.github.io/ComfyUI_examples/wan22/)
 - Audio Models
    - [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
    - [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
@@ -84,9 +85,9 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [Hunyuan3D 2.0](https://docs.comfy.org/tutorials/3d/hunyuan3D-2)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
-- Smart memory management: can automatically run models on GPUs with as low as 1GB vram.
+- Smart memory management: can automatically run large models on GPUs with as low as 1GB vram with smart offloading.
 - Works even if you don't have a GPU with: ```--cpu``` (slow)
-- Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models.
+- Can load ckpt and safetensors: All in one checkpoints or standalone diffusion models, VAEs and CLIP models.
 - Safe loading of ckpt, pt, pth, etc.. files.
 - Embeddings/Textual inversion
 - [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
@@ -98,7 +99,6 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
 - [Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/) with both regular and inpainting models.
 - [ControlNet and T2I-Adapter](https://comfyanonymous.github.io/ComfyUI_examples/controlnet/)
 - [Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)](https://comfyanonymous.github.io/ComfyUI_examples/upscale_models/)
-- [unCLIP Models](https://comfyanonymous.github.io/ComfyUI_examples/unclip/)
 - [GLIGEN](https://comfyanonymous.github.io/ComfyUI_examples/gligen/)
 - [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/)
 - [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)

comfy/latent_formats.py

@@ -457,6 +457,82 @@ class Wan21(LatentFormat):
         latents_std = self.latents_std.to(latent.device, latent.dtype)
         return latent * latents_std / self.scale_factor + latents_mean
 
+class Wan22(Wan21):
+    latent_channels = 48
+    latent_dimensions = 3
+
+    latent_rgb_factors = [
+            [ 0.0119,  0.0103,  0.0046],
+            [-0.1062, -0.0504,  0.0165],
+            [ 0.0140,  0.0409,  0.0491],
+            [-0.0813, -0.0677,  0.0607],
+            [ 0.0656,  0.0851,  0.0808],
+            [ 0.0264,  0.0463,  0.0912],
+            [ 0.0295,  0.0326,  0.0590],
+            [-0.0244, -0.0270,  0.0025],
+            [ 0.0443, -0.0102,  0.0288],
+            [-0.0465, -0.0090, -0.0205],
+            [ 0.0359,  0.0236,  0.0082],
+            [-0.0776,  0.0854,  0.1048],
+            [ 0.0564,  0.0264,  0.0561],
+            [ 0.0006,  0.0594,  0.0418],
+            [-0.0319, -0.0542, -0.0637],
+            [-0.0268,  0.0024,  0.0260],
+            [ 0.0539,  0.0265,  0.0358],
+            [-0.0359, -0.0312, -0.0287],
+            [-0.0285, -0.1032, -0.1237],
+            [ 0.1041,  0.0537,  0.0622],
+            [-0.0086, -0.0374, -0.0051],
+            [ 0.0390,  0.0670,  0.2863],
+            [ 0.0069,  0.0144,  0.0082],
+            [ 0.0006, -0.0167,  0.0079],
+            [ 0.0313, -0.0574, -0.0232],
+            [-0.1454, -0.0902, -0.0481],
+            [ 0.0714,  0.0827,  0.0447],
+            [-0.0304, -0.0574, -0.0196],
+            [ 0.0401,  0.0384,  0.0204],
+            [-0.0758, -0.0297, -0.0014],
+            [ 0.0568,  0.1307,  0.1372],
+            [-0.0055, -0.0310, -0.0380],
+            [ 0.0239, -0.0305,  0.0325],
+            [-0.0663, -0.0673, -0.0140],
+            [-0.0416, -0.0047, -0.0023],
+            [ 0.0166,  0.0112, -0.0093],
+            [-0.0211,  0.0011,  0.0331],
+            [ 0.1833,  0.1466,  0.2250],
+            [-0.0368,  0.0370,  0.0295],
+            [-0.3441, -0.3543, -0.2008],
+            [-0.0479, -0.0489, -0.0420],
+            [-0.0660, -0.0153,  0.0800],
+            [-0.0101,  0.0068,  0.0156],
+            [-0.0690, -0.0452, -0.0927],
+            [-0.0145,  0.0041,  0.0015],
+            [ 0.0421,  0.0451,  0.0373],
+            [ 0.0504, -0.0483, -0.0356],
+            [-0.0837,  0.0168,  0.0055]
+        ]
+
+    latent_rgb_factors_bias = [0.0317, -0.0878, -0.1388]
+
+    def __init__(self):
+        self.scale_factor = 1.0
+        self.latents_mean = torch.tensor([
+                -0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557,
+                -0.1382, 0.0542, 0.2813, 0.0891, 0.1570, -0.0098, 0.0375, -0.1825,
+                -0.2246, -0.1207, -0.0698, 0.5109, 0.2665, -0.2108, -0.2158, 0.2502,
+                -0.2055, -0.0322, 0.1109, 0.1567, -0.0729, 0.0899, -0.2799, -0.1230,
+                -0.0313, -0.1649, 0.0117, 0.0723, -0.2839, -0.2083, -0.0520, 0.3748,
+                0.0152, 0.1957, 0.1433, -0.2944, 0.3573, -0.0548, -0.1681, -0.0667,
+            ]).view(1, self.latent_channels, 1, 1, 1)
+        self.latents_std = torch.tensor([
+                0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.4990, 0.4818, 0.5013,
+                0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978,
+                0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659,
+                0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093,
+                0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887,
+                0.3971, 1.0600, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744
+            ]).view(1, self.latent_channels, 1, 1, 1)
+
 class Hunyuan3Dv2(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1

comfy/ldm/wan/model.py

@@ -201,8 +201,10 @@ class WanAttentionBlock(nn.Module):
             freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
         """
         # assert e.dtype == torch.float32
-
+        if e.ndim < 4:
             e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
+        else:
+            e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2)
         # assert e[0].dtype == torch.float32
 
         # self-attention
@@ -325,7 +327,10 @@ class Head(nn.Module):
             e(Tensor): Shape [B, C]
         """
         # assert e.dtype == torch.float32
+        if e.ndim < 3:
             e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1)
+        else:
+            e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e.unsqueeze(2)).unbind(2)
         x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
         return x
 
@@ -506,8 +511,9 @@ class WanModel(torch.nn.Module):
 
         # time embeddings
         e = self.time_embedding(
-            sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype))
+            sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))
-        e0 = self.time_projection(e).unflatten(1, (6, self.dim))
+        e = e.reshape(t.shape[0], -1, e.shape[-1])
+        e0 = self.time_projection(e).unflatten(2, (6, self.dim))
 
         # context
         context = self.text_embedding(context)

comfy/ldm/wan/vae2_2.py

@@ -0,0 +1,726 @@
+# original version: https://github.com/Wan-Video/Wan2.2/blob/main/wan/modules/vae2_2.py
+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from .vae import AttentionBlock, CausalConv3d, RMS_norm
+
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+CACHE_T = 2
+
+
+class Resample(nn.Module):
+
+    def __init__(self, dim, mode):
+        assert mode in (
+            "none",
+            "upsample2d",
+            "upsample3d",
+            "downsample2d",
+            "downsample3d",
+        )
+        super().__init__()
+        self.dim = dim
+        self.mode = mode
+
+        # layers
+        if mode == "upsample2d":
+            self.resample = nn.Sequential(
+                nn.Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"),
+                ops.Conv2d(dim, dim, 3, padding=1),
+            )
+        elif mode == "upsample3d":
+            self.resample = nn.Sequential(
+                nn.Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"),
+                ops.Conv2d(dim, dim, 3, padding=1),
+                # ops.Conv2d(dim, dim//2, 3, padding=1)
+            )
+            self.time_conv = CausalConv3d(
+                dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
+        elif mode == "downsample2d":
+            self.resample = nn.Sequential(
+                nn.ZeroPad2d((0, 1, 0, 1)),
+                ops.Conv2d(dim, dim, 3, stride=(2, 2)))
+        elif mode == "downsample3d":
+            self.resample = nn.Sequential(
+                nn.ZeroPad2d((0, 1, 0, 1)),
+                ops.Conv2d(dim, dim, 3, stride=(2, 2)))
+            self.time_conv = CausalConv3d(
+                dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))
+        else:
+            self.resample = nn.Identity()
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        b, c, t, h, w = x.size()
+        if self.mode == "upsample3d":
+            if feat_cache is not None:
+                idx = feat_idx[0]
+                if feat_cache[idx] is None:
+                    feat_cache[idx] = "Rep"
+                    feat_idx[0] += 1
+                else:
+                    cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                    if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and
+                            feat_cache[idx] != "Rep"):
+                        # cache last frame of last two chunk
+                        cache_x = torch.cat(
+                            [
+                                feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                                    cache_x.device),
+                                cache_x,
+                            ],
+                            dim=2,
+                        )
+                    if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and
+                            feat_cache[idx] == "Rep"):
+                        cache_x = torch.cat(
+                            [
+                                torch.zeros_like(cache_x).to(cache_x.device),
+                                cache_x
+                            ],
+                            dim=2,
+                        )
+                    if feat_cache[idx] == "Rep":
+                        x = self.time_conv(x)
+                    else:
+                        x = self.time_conv(x, feat_cache[idx])
+                    feat_cache[idx] = cache_x
+                    feat_idx[0] += 1
+                    x = x.reshape(b, 2, c, t, h, w)
+                    x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]),
+                                    3)
+                    x = x.reshape(b, c, t * 2, h, w)
+        t = x.shape[2]
+        x = rearrange(x, "b c t h w -> (b t) c h w")
+        x = self.resample(x)
+        x = rearrange(x, "(b t) c h w -> b c t h w", t=t)
+
+        if self.mode == "downsample3d":
+            if feat_cache is not None:
+                idx = feat_idx[0]
+                if feat_cache[idx] is None:
+                    feat_cache[idx] = x.clone()
+                    feat_idx[0] += 1
+                else:
+                    cache_x = x[:, :, -1:, :, :].clone()
+                    x = self.time_conv(
+                        torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
+                    feat_cache[idx] = cache_x
+                    feat_idx[0] += 1
+        return x
+
+
+class ResidualBlock(nn.Module):
+
+    def __init__(self, in_dim, out_dim, dropout=0.0):
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        # layers
+        self.residual = nn.Sequential(
+            RMS_norm(in_dim, images=False),
+            nn.SiLU(),
+            CausalConv3d(in_dim, out_dim, 3, padding=1),
+            RMS_norm(out_dim, images=False),
+            nn.SiLU(),
+            nn.Dropout(dropout),
+            CausalConv3d(out_dim, out_dim, 3, padding=1),
+        )
+        self.shortcut = (
+            CausalConv3d(in_dim, out_dim, 1)
+            if in_dim != out_dim else nn.Identity())
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        h = self.shortcut(x)
+        for layer in self.residual:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat(
+                        [
+                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                                cache_x.device),
+                            cache_x,
+                        ],
+                        dim=2,
+                    )
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x + h
+
+
+def patchify(x, patch_size):
+    if patch_size == 1:
+        return x
+    if x.dim() == 4:
+        x = rearrange(
+            x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size, r=patch_size)
+    elif x.dim() == 5:
+        x = rearrange(
+            x,
+            "b c f (h q) (w r) -> b (c r q) f h w",
+            q=patch_size,
+            r=patch_size,
+        )
+    else:
+        raise ValueError(f"Invalid input shape: {x.shape}")
+
+    return x
+
+
+def unpatchify(x, patch_size):
+    if patch_size == 1:
+        return x
+
+    if x.dim() == 4:
+        x = rearrange(
+            x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size, r=patch_size)
+    elif x.dim() == 5:
+        x = rearrange(
+            x,
+            "b (c r q) f h w -> b c f (h q) (w r)",
+            q=patch_size,
+            r=patch_size,
+        )
+    return x
+
+
+class AvgDown3D(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        factor_t,
+        factor_s=1,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.factor_t = factor_t
+        self.factor_s = factor_s
+        self.factor = self.factor_t * self.factor_s * self.factor_s
+
+        assert in_channels * self.factor % out_channels == 0
+        self.group_size = in_channels * self.factor // out_channels
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        pad_t = (self.factor_t - x.shape[2] % self.factor_t) % self.factor_t
+        pad = (0, 0, 0, 0, pad_t, 0)
+        x = F.pad(x, pad)
+        B, C, T, H, W = x.shape
+        x = x.view(
+            B,
+            C,
+            T // self.factor_t,
+            self.factor_t,
+            H // self.factor_s,
+            self.factor_s,
+            W // self.factor_s,
+            self.factor_s,
+        )
+        x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous()
+        x = x.view(
+            B,
+            C * self.factor,
+            T // self.factor_t,
+            H // self.factor_s,
+            W // self.factor_s,
+        )
+        x = x.view(
+            B,
+            self.out_channels,
+            self.group_size,
+            T // self.factor_t,
+            H // self.factor_s,
+            W // self.factor_s,
+        )
+        x = x.mean(dim=2)
+        return x
+
+
+class DupUp3D(nn.Module):
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        factor_t,
+        factor_s=1,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        self.factor_t = factor_t
+        self.factor_s = factor_s
+        self.factor = self.factor_t * self.factor_s * self.factor_s
+
+        assert out_channels * self.factor % in_channels == 0
+        self.repeats = out_channels * self.factor // in_channels
+
+    def forward(self, x: torch.Tensor, first_chunk=False) -> torch.Tensor:
+        x = x.repeat_interleave(self.repeats, dim=1)
+        x = x.view(
+            x.size(0),
+            self.out_channels,
+            self.factor_t,
+            self.factor_s,
+            self.factor_s,
+            x.size(2),
+            x.size(3),
+            x.size(4),
+        )
+        x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous()
+        x = x.view(
+            x.size(0),
+            self.out_channels,
+            x.size(2) * self.factor_t,
+            x.size(4) * self.factor_s,
+            x.size(6) * self.factor_s,
+        )
+        if first_chunk:
+            x = x[:, :, self.factor_t - 1:, :, :]
+        return x
+
+
+class Down_ResidualBlock(nn.Module):
+
+    def __init__(self,
+                 in_dim,
+                 out_dim,
+                 dropout,
+                 mult,
+                 temperal_downsample=False,
+                 down_flag=False):
+        super().__init__()
+
+        # Shortcut path with downsample
+        self.avg_shortcut = AvgDown3D(
+            in_dim,
+            out_dim,
+            factor_t=2 if temperal_downsample else 1,
+            factor_s=2 if down_flag else 1,
+        )
+
+        # Main path with residual blocks and downsample
+        downsamples = []
+        for _ in range(mult):
+            downsamples.append(ResidualBlock(in_dim, out_dim, dropout))
+            in_dim = out_dim
+
+        # Add the final downsample block
+        if down_flag:
+            mode = "downsample3d" if temperal_downsample else "downsample2d"
+            downsamples.append(Resample(out_dim, mode=mode))
+
+        self.downsamples = nn.Sequential(*downsamples)
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        x_copy = x.clone()
+        for module in self.downsamples:
+            x = module(x, feat_cache, feat_idx)
+
+        return x + self.avg_shortcut(x_copy)
+
+
+class Up_ResidualBlock(nn.Module):
+
+    def __init__(self,
+                 in_dim,
+                 out_dim,
+                 dropout,
+                 mult,
+                 temperal_upsample=False,
+                 up_flag=False):
+        super().__init__()
+        # Shortcut path with upsample
+        if up_flag:
+            self.avg_shortcut = DupUp3D(
+                in_dim,
+                out_dim,
+                factor_t=2 if temperal_upsample else 1,
+                factor_s=2 if up_flag else 1,
+            )
+        else:
+            self.avg_shortcut = None
+
+        # Main path with residual blocks and upsample
+        upsamples = []
+        for _ in range(mult):
+            upsamples.append(ResidualBlock(in_dim, out_dim, dropout))
+            in_dim = out_dim
+
+        # Add the final upsample block
+        if up_flag:
+            mode = "upsample3d" if temperal_upsample else "upsample2d"
+            upsamples.append(Resample(out_dim, mode=mode))
+
+        self.upsamples = nn.Sequential(*upsamples)
+
+    def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
+        x_main = x.clone()
+        for module in self.upsamples:
+            x_main = module(x_main, feat_cache, feat_idx)
+        if self.avg_shortcut is not None:
+            x_shortcut = self.avg_shortcut(x, first_chunk)
+            return x_main + x_shortcut
+        else:
+            return x_main
+
+
+class Encoder3d(nn.Module):
+
+    def __init__(
+        self,
+        dim=128,
+        z_dim=4,
+        dim_mult=[1, 2, 4, 4],
+        num_res_blocks=2,
+        attn_scales=[],
+        temperal_downsample=[True, True, False],
+        dropout=0.0,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+
+        # dimensions
+        dims = [dim * u for u in [1] + dim_mult]
+        scale = 1.0
+
+        # init block
+        self.conv1 = CausalConv3d(12, dims[0], 3, padding=1)
+
+        # downsample blocks
+        downsamples = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            t_down_flag = (
+                temperal_downsample[i]
+                if i < len(temperal_downsample) else False)
+            downsamples.append(
+                Down_ResidualBlock(
+                    in_dim=in_dim,
+                    out_dim=out_dim,
+                    dropout=dropout,
+                    mult=num_res_blocks,
+                    temperal_downsample=t_down_flag,
+                    down_flag=i != len(dim_mult) - 1,
+                ))
+            scale /= 2.0
+        self.downsamples = nn.Sequential(*downsamples)
+
+        # middle blocks
+        self.middle = nn.Sequential(
+            ResidualBlock(out_dim, out_dim, dropout),
+            AttentionBlock(out_dim),
+            ResidualBlock(out_dim, out_dim, dropout),
+        )
+
+        # # output blocks
+        self.head = nn.Sequential(
+            RMS_norm(out_dim, images=False),
+            nn.SiLU(),
+            CausalConv3d(out_dim, z_dim, 3, padding=1),
+        )
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat(
+                    [
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device),
+                        cache_x,
+                    ],
+                    dim=2,
+                )
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        ## downsamples
+        for layer in self.downsamples:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## middle
+        for layer in self.middle:
+            if isinstance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## head
+        for layer in self.head:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    cache_x = torch.cat(
+                        [
+                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                                cache_x.device),
+                            cache_x,
+                        ],
+                        dim=2,
+                    )
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+
+        return x
+
+
+class Decoder3d(nn.Module):
+
+    def __init__(
+        self,
+        dim=128,
+        z_dim=4,
+        dim_mult=[1, 2, 4, 4],
+        num_res_blocks=2,
+        attn_scales=[],
+        temperal_upsample=[False, True, True],
+        dropout=0.0,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_upsample = temperal_upsample
+
+        # dimensions
+        dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
+        # init block
+        self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)
+
+        # middle blocks
+        self.middle = nn.Sequential(
+            ResidualBlock(dims[0], dims[0], dropout),
+            AttentionBlock(dims[0]),
+            ResidualBlock(dims[0], dims[0], dropout),
+        )
+
+        # upsample blocks
+        upsamples = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            t_up_flag = temperal_upsample[i] if i < len(
+                temperal_upsample) else False
+            upsamples.append(
+                Up_ResidualBlock(
+                    in_dim=in_dim,
+                    out_dim=out_dim,
+                    dropout=dropout,
+                    mult=num_res_blocks + 1,
+                    temperal_upsample=t_up_flag,
+                    up_flag=i != len(dim_mult) - 1,
+                ))
+        self.upsamples = nn.Sequential(*upsamples)
+
+        # output blocks
+        self.head = nn.Sequential(
+            RMS_norm(out_dim, images=False),
+            nn.SiLU(),
+            CausalConv3d(out_dim, 12, 3, padding=1),
+        )
+
+    def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat(
+                    [
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device),
+                        cache_x,
+                    ],
+                    dim=2,
+                )
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        for layer in self.middle:
+            if isinstance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## upsamples
+        for layer in self.upsamples:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx, first_chunk)
+            else:
+                x = layer(x)
+
+        ## head
+        for layer in self.head:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    cache_x = torch.cat(
+                        [
+                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                                cache_x.device),
+                            cache_x,
+                        ],
+                        dim=2,
+                    )
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x
+
+
+def count_conv3d(model):
+    count = 0
+    for m in model.modules():
+        if isinstance(m, CausalConv3d):
+            count += 1
+    return count
+
+
+class WanVAE(nn.Module):
+
+    def __init__(
+        self,
+        dim=160,
+        dec_dim=256,
+        z_dim=16,
+        dim_mult=[1, 2, 4, 4],
+        num_res_blocks=2,
+        attn_scales=[],
+        temperal_downsample=[True, True, False],
+        dropout=0.0,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+        self.temperal_upsample = temperal_downsample[::-1]
+
+        # modules
+        self.encoder = Encoder3d(
+            dim,
+            z_dim * 2,
+            dim_mult,
+            num_res_blocks,
+            attn_scales,
+            self.temperal_downsample,
+            dropout,
+        )
+        self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
+        self.conv2 = CausalConv3d(z_dim, z_dim, 1)
+        self.decoder = Decoder3d(
+            dec_dim,
+            z_dim,
+            dim_mult,
+            num_res_blocks,
+            attn_scales,
+            self.temperal_upsample,
+            dropout,
+        )
+
+    def encode(self, x):
+        self.clear_cache()
+        x = patchify(x, patch_size=2)
+        t = x.shape[2]
+        iter_ = 1 + (t - 1) // 4
+        for i in range(iter_):
+            self._enc_conv_idx = [0]
+            if i == 0:
+                out = self.encoder(
+                    x[:, :, :1, :, :],
+                    feat_cache=self._enc_feat_map,
+                    feat_idx=self._enc_conv_idx,
+                )
+            else:
+                out_ = self.encoder(
+                    x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
+                    feat_cache=self._enc_feat_map,
+                    feat_idx=self._enc_conv_idx,
+                )
+                out = torch.cat([out, out_], 2)
+        mu, log_var = self.conv1(out).chunk(2, dim=1)
+        self.clear_cache()
+        return mu
+
+    def decode(self, z):
+        self.clear_cache()
+        iter_ = z.shape[2]
+        x = self.conv2(z)
+        for i in range(iter_):
+            self._conv_idx = [0]
+            if i == 0:
+                out = self.decoder(
+                    x[:, :, i:i + 1, :, :],
+                    feat_cache=self._feat_map,
+                    feat_idx=self._conv_idx,
+                    first_chunk=True,
+                )
+            else:
+                out_ = self.decoder(
+                    x[:, :, i:i + 1, :, :],
+                    feat_cache=self._feat_map,
+                    feat_idx=self._conv_idx,
+                )
+                out = torch.cat([out, out_], 2)
+        out = unpatchify(out, patch_size=2)
+        self.clear_cache()
+        return out
+
+    def reparameterize(self, mu, log_var):
+        std = torch.exp(0.5 * log_var)
+        eps = torch.randn_like(std)
+        return eps * std + mu
+
+    def sample(self, imgs, deterministic=False):
+        mu, log_var = self.encode(imgs)
+        if deterministic:
+            return mu
+        std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0))
+        return mu + std * torch.randn_like(std)
+
+    def clear_cache(self):
+        self._conv_num = count_conv3d(self.decoder)
+        self._conv_idx = [0]
+        self._feat_map = [None] * self._conv_num
+        # cache encode
+        self._enc_conv_num = count_conv3d(self.encoder)
+        self._enc_conv_idx = [0]
+        self._enc_feat_map = [None] * self._enc_conv_num

comfy/model_base.py

@@ -1097,6 +1097,7 @@ class WAN21(BaseModel):
                 image[:, i: i + 16] = self.process_latent_in(image[:, i: i + 16])
             image = utils.resize_to_batch_size(image, noise.shape[0])
 
+        if extra_channels != image.shape[1] + 4:
             if not self.image_to_video or extra_channels == image.shape[1]:
                 return image
 
@@ -1182,6 +1183,31 @@ class WAN21_Camera(WAN21):
             out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions)
         return out
 
+class WAN22(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)
+        self.image_to_video = image_to_video
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+        if denoise_mask is not None:
+            out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask)
+        return out
+
+    def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):
+        if denoise_mask is None:
+            return timestep
+        temp_ts = (torch.mean(denoise_mask[:, :, :, ::2, ::2], dim=1, keepdim=True) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1))).reshape(timestep.shape[0], -1)
+        return temp_ts
+
+    def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
+        return latent_image
+
 class Hunyuan3Dv2(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3d.model.Hunyuan3Dv2)

comfy/model_detection.py

@@ -346,7 +346,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config = {}
         dit_config["image_model"] = "wan2.1"
         dim = state_dict['{}head.modulation'.format(key_prefix)].shape[-1]
+        out_dim = state_dict['{}head.head.weight'.format(key_prefix)].shape[0] // 4
         dit_config["dim"] = dim
+        dit_config["out_dim"] = out_dim
         dit_config["num_heads"] = dim // 128
         dit_config["ffn_dim"] = state_dict['{}blocks.0.ffn.0.weight'.format(key_prefix)].shape[0]
         dit_config["num_layers"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')

comfy/sd.py

@@ -14,6 +14,7 @@ import comfy.ldm.genmo.vae.model
 import comfy.ldm.lightricks.vae.causal_video_autoencoder
 import comfy.ldm.cosmos.vae
 import comfy.ldm.wan.vae
+import comfy.ldm.wan.vae2_2
 import comfy.ldm.hunyuan3d.vae
 import comfy.ldm.ace.vae.music_dcae_pipeline
 import yaml
@@ -420,6 +421,19 @@ class VAE:
                 self.memory_used_encode = lambda shape, dtype: (50 * (round((shape[2] + 7) / 8) * 8) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                 self.working_dtypes = [torch.bfloat16, torch.float32]
             elif "decoder.middle.0.residual.0.gamma" in sd:
+                if "decoder.upsamples.0.upsamples.0.residual.2.weight" in sd:  # Wan 2.2 VAE
+                    self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16)
+                    self.upscale_index_formula = (4, 16, 16)
+                    self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)
+                    self.downscale_index_formula = (4, 16, 16)
+                    self.latent_dim = 3
+                    self.latent_channels = 48
+                    ddconfig = {"dim": 160, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0}
+                    self.first_stage_model = comfy.ldm.wan.vae2_2.WanVAE(**ddconfig)
+                    self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+                    self.memory_used_encode = lambda shape, dtype: 3300 * shape[3] * shape[4] * model_management.dtype_size(dtype)
+                    self.memory_used_decode = lambda shape, dtype: 8000 * shape[3] * shape[4] * (16 * 16) * model_management.dtype_size(dtype)
+                else:  # Wan 2.1 VAE
                     self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
                     self.upscale_index_formula = (4, 8, 8)
                     self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)

comfy/supported_models.py

@@ -1059,6 +1059,19 @@ class WAN21_Vace(WAN21_T2V):
         out = model_base.WAN21_Vace(self, image_to_video=False, device=device)
         return out
 
+class WAN22_T2V(WAN21_T2V):
+    unet_config = {
+        "image_model": "wan2.1",
+        "model_type": "t2v",
+        "out_dim": 48,
+    }
+
+    latent_format = latent_formats.Wan22
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.WAN22(self, image_to_video=True, device=device)
+        return out
+
 class Hunyuan3Dv2(supported_models_base.BASE):
     unet_config = {
         "image_model": "hunyuan3d2",
@@ -1217,6 +1230,6 @@ class Omnigen2(supported_models_base.BASE):
         return supported_models_base.ClipTarget(comfy.text_encoders.omnigen2.Omnigen2Tokenizer, comfy.text_encoders.omnigen2.te(**hunyuan_detect))
 
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep, Omnigen2]
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep, Omnigen2]
 
 models += [SVD_img2vid]

comfy_api/input/video_types.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from typing import Optional, Union
 import io
+import av
 from comfy_api.util import VideoContainer, VideoCodec, VideoComponents
 
 class VideoInput(ABC):
@@ -70,3 +71,15 @@ class VideoInput(ABC):
         components = self.get_components()
         frame_count = components.images.shape[0]
         return float(frame_count / components.frame_rate)
+
+    def get_container_format(self) -> str:
+        """
+        Returns the container format of the video (e.g., 'mp4', 'mov', 'avi').
+
+        Returns:
+            Container format as string
+        """
+        # Default implementation - subclasses should override for better performance
+        source = self.get_stream_source()
+        with av.open(source, mode="r") as container:
+            return container.format.name

comfy_api/input_impl/video_types.py

@@ -121,6 +121,18 @@ class VideoFromFile(VideoInput):
 
         raise ValueError(f"Could not determine duration for file '{self.__file}'")
 
+    def get_container_format(self) -> str:
+        """
+        Returns the container format of the video (e.g., 'mp4', 'mov', 'avi').
+
+        Returns:
+            Container format as string
+        """
+        if isinstance(self.__file, io.BytesIO):
+            self.__file.seek(0)
+        with av.open(self.__file, mode='r') as container:
+            return container.format.name
+
     def get_components_internal(self, container: InputContainer) -> VideoComponents:
         # Get video frames
         frames = []

comfy_api_nodes/nodes_moonvalley.py

@@ -5,7 +5,6 @@ import torch
 from comfy_api_nodes.util.validation_utils import (
     get_image_dimensions,
     validate_image_dimensions,
-    validate_video_dimensions,
 )
 
 
@@ -176,54 +175,76 @@ def validate_input_image(
     )
 
 
-def validate_input_video(
+def validate_video_to_video_input(video: VideoInput) -> VideoInput:
-    video: VideoInput, num_frames_out: int, with_frame_conditioning: bool = False
+    """
-):
+    Validates and processes video input for Moonvalley Video-to-Video generation.
+
+    Args:
+        video: Input video to validate
+
+    Returns:
+        Validated and potentially trimmed video
+
+    Raises:
+        ValueError: If video doesn't meet requirements
+        MoonvalleyApiError: If video duration is too short
+    """
+    width, height = _get_video_dimensions(video)
+    _validate_video_dimensions(width, height)
+    _validate_container_format(video)
+
+    return _validate_and_trim_duration(video)
+
+
+def _get_video_dimensions(video: VideoInput) -> tuple[int, int]:
+    """Extracts video dimensions with error handling."""
     try:
-        width, height = video.get_dimensions()
+        return video.get_dimensions()
     except Exception as e:
         logging.error("Error getting dimensions of video: %s", e)
         raise ValueError(f"Cannot get video dimensions: {e}") from e
 
-    validate_input_media(width, height, with_frame_conditioning)
-    validate_video_dimensions(
-        video,
-        min_width=MIN_VID_WIDTH,
-        min_height=MIN_VID_HEIGHT,
-        max_width=MAX_VID_WIDTH,
-        max_height=MAX_VID_HEIGHT,
-    )
 
-    trimmed_video = validate_input_video_length(video, num_frames_out)
+def _validate_video_dimensions(width: int, height: int) -> None:
-    return trimmed_video
+    """Validates video dimensions meet Moonvalley V2V requirements."""
+    supported_resolutions = {
+        (1920, 1080), (1080, 1920), (1152, 1152),
+        (1536, 1152), (1152, 1536)
+    }
+
+    if (width, height) not in supported_resolutions:
+        supported_list = ', '.join([f'{w}x{h}' for w, h in sorted(supported_resolutions)])
+        raise ValueError(f"Resolution {width}x{height} not supported. Supported: {supported_list}")
 
 
-def validate_input_video_length(video: VideoInput, num_frames: int):
+def _validate_container_format(video: VideoInput) -> None:
+    """Validates video container format is MP4."""
+    container_format = video.get_container_format()
+    if container_format not in ['mp4', 'mov,mp4,m4a,3gp,3g2,mj2']:
+        raise ValueError(f"Only MP4 container format supported. Got: {container_format}")
 
-    if video.get_duration() > 60:
-        raise MoonvalleyApiError(
-            "Input Video lenth should be less than 1min. Please trim."
-        )
 
-    if num_frames == 128:
+def _validate_and_trim_duration(video: VideoInput) -> VideoInput:
-        if video.get_duration() < 5:
+    """Validates video duration and trims to 5 seconds if needed."""
-            raise MoonvalleyApiError(
+    duration = video.get_duration()
-                "Input Video length is less than 5s. Please use a video longer than or equal to 5s."
+    _validate_minimum_duration(duration)
-            )
+    return _trim_if_too_long(video, duration)
-        if video.get_duration() > 5:
+
-            #    trim video to 5s
+
-            video = trim_video(video, 5)
+def _validate_minimum_duration(duration: float) -> None:
-    if num_frames == 256:
+    """Ensures video is at least 5 seconds long."""
-        if video.get_duration() < 10:
+    if duration < 5:
-            raise MoonvalleyApiError(
+        raise MoonvalleyApiError("Input video must be at least 5 seconds long.")
-                "Input Video length is less than 10s. Please use a video longer than or equal to 10s."
+
-            )
+
-        if video.get_duration() > 10:
+def _trim_if_too_long(video: VideoInput, duration: float) -> VideoInput:
-            # trim video to 10s
+    """Trims video to 5 seconds if longer."""
-            video = trim_video(video, 10)
+    if duration > 5:
+        return trim_video(video, 5)
     return video
 
 
+
 def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
     """
     Returns a new VideoInput object trimmed from the beginning to the specified duration,
@@ -278,15 +299,13 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
                     f"Added audio stream: {stream.sample_rate}Hz, {stream.channels} channels"
                 )
 
-        # Calculate target frame count that's divisible by 32
+        # Calculate target frame count that's divisible by 16
         fps = input_container.streams.video[0].average_rate
         estimated_frames = int(duration_sec * fps)
-        target_frames = (
+        target_frames = (estimated_frames // 16) * 16  # Round down to nearest multiple of 16
-            estimated_frames // 32
-        ) * 32  # Round down to nearest multiple of 32
 
         if target_frames == 0:
-            raise ValueError("Video too short: need at least 32 frames for Moonvalley")
+            raise ValueError("Video too short: need at least 16 frames for Moonvalley")
 
         frame_count = 0
         audio_frame_count = 0
@@ -353,8 +372,8 @@ class BaseMoonvalleyVideoNode:
             "16:9 (1920 x 1080)": {"width": 1920, "height": 1080},
             "9:16 (1080 x 1920)": {"width": 1080, "height": 1920},
             "1:1 (1152 x 1152)": {"width": 1152, "height": 1152},
-            "4:3 (1440 x 1080)": {"width": 1440, "height": 1080},
+            "4:3 (1536 x 1152)": {"width": 1536, "height": 1152},
-            "3:4 (1080 x 1440)": {"width": 1080, "height": 1440},
+            "3:4 (1152 x 1536)": {"width": 1152, "height": 1536},
             "21:9 (2560 x 1080)": {"width": 2560, "height": 1080},
         }
         if resolution in res_map:
@@ -494,7 +513,6 @@ class MoonvalleyImg2VideoNode(BaseMoonvalleyVideoNode):
         image = kwargs.get("image", None)
         if image is None:
             raise MoonvalleyApiError("image is required")
-        total_frames = get_total_frames_from_length()
 
         validate_input_image(image, True)
         validate_prompts(prompt, negative_prompt, MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
@@ -505,7 +523,7 @@ class MoonvalleyImg2VideoNode(BaseMoonvalleyVideoNode):
             steps=kwargs.get("steps"),
             seed=kwargs.get("seed"),
             guidance_scale=kwargs.get("prompt_adherence"),
-            num_frames=total_frames,
+            num_frames=128,
             width=width_height.get("width"),
             height=width_height.get("height"),
             use_negative_prompts=True,
@@ -549,21 +567,28 @@ class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode):
 
     @classmethod
     def INPUT_TYPES(cls):
-        input_types = super().INPUT_TYPES()
+        return {
-        for param in ["resolution", "image"]:
+            "required": {
-            if param in input_types["required"]:
+                "prompt": model_field_to_node_input(
-                del input_types["required"][param]
+                    IO.STRING, MoonvalleyVideoToVideoRequest, "prompt_text",
-            if param in input_types["optional"]:
+                    multiline=True
-                del input_types["optional"][param]
-        input_types["optional"] = {
-            "video": (
-                IO.VIDEO,
-                {
-                    "default": "",
-                    "multiline": False,
-                    "tooltip": "The reference video used to generate the output video. Input a 5s video for 128 frames and a 10s video for 256 frames. Longer videos will be trimmed automatically.",
-                },
                 ),
+                "negative_prompt": model_field_to_node_input(
+                    IO.STRING,
+                    MoonvalleyVideoToVideoInferenceParams,
+                    "negative_prompt",
+                    multiline=True,
+                    default="low-poly, flat shader, bad rigging, stiff animation, uncanny eyes, low-quality textures, looping glitch, cheap effect, overbloom, bloom spam, default lighting, game asset, stiff face, ugly specular, AI artifacts"
+                ),
+                "seed": model_field_to_node_input(IO.INT,MoonvalleyVideoToVideoInferenceParams, "seed", default=random.randint(0, 2**32 - 1), min=0, max=4294967295, step=1, display="number", tooltip="Random seed value", control_after_generate=True),
+            },
+            "hidden": {
+                "auth_token": "AUTH_TOKEN_COMFY_ORG",
+                "comfy_api_key": "API_KEY_COMFY_ORG",
+                "unique_id": "UNIQUE_ID",
+            },
+            "optional": {
+                "video": (IO.VIDEO, {"default": "", "multiline": False, "tooltip": "The reference video used to generate the output video. Must be at least 5 seconds long. Videos longer than 5s will be automatically trimmed. Only MP4 format supported."}),
                 "control_type": (
                     ["Motion Transfer", "Pose Transfer"],
                     {"default": "Motion Transfer"},
@@ -577,10 +602,9 @@ class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode):
                         "max": 100,
                         "tooltip": "Only used if control_type is 'Motion Transfer'",
                     },
-            ),
+                )
+            }
         }
-
-        return input_types
 
     RETURN_TYPES = ("VIDEO",)
     RETURN_NAMES = ("video",)
@@ -589,15 +613,13 @@ class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode):
         self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs
     ):
         video = kwargs.get("video")
-        num_frames = get_total_frames_from_length()
 
         if not video:
             raise MoonvalleyApiError("video is required")
 
-        """Validate video input"""
         video_url = ""
         if video:
-            validated_video = validate_input_video(video, num_frames, False)
+            validated_video = validate_video_to_video_input(video)
             video_url = upload_video_to_comfyapi(validated_video, auth_kwargs=kwargs)
 
         control_type = kwargs.get("control_type")
@@ -605,12 +627,16 @@ class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode):
 
         """Validate prompts and inference input"""
         validate_prompts(prompt, negative_prompt)
-        inference_params = MoonvalleyVideoToVideoInferenceParams(
+
+        # Only include motion_intensity for Motion Transfer
+        control_params = {}
+        if control_type == "Motion Transfer" and motion_intensity is not None:
+            control_params['motion_intensity'] = motion_intensity
+
+        inference_params=MoonvalleyVideoToVideoInferenceParams(
             negative_prompt=negative_prompt,
-            steps=kwargs.get("steps"),
             seed=kwargs.get("seed"),
-            guidance_scale=kwargs.get("prompt_adherence"),
+            control_params=control_params
-            control_params={"motion_intensity": motion_intensity},
         )
 
         control = self.parseControlParameter(control_type)
@@ -667,14 +693,13 @@ class MoonvalleyTxt2VideoNode(BaseMoonvalleyVideoNode):
     ):
         validate_prompts(prompt, negative_prompt, MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
         width_height = self.parseWidthHeightFromRes(kwargs.get("resolution"))
-        num_frames = get_total_frames_from_length()
 
-        inference_params = MoonvalleyTextToVideoInferenceParams(
+        inference_params=MoonvalleyTextToVideoInferenceParams(
                     negative_prompt=negative_prompt,
                     steps=kwargs.get("steps"),
                     seed=kwargs.get("seed"),
                     guidance_scale=kwargs.get("prompt_adherence"),
-            num_frames=num_frames,
+                    num_frames=128,
                     width=width_height.get("width"),
                     height=width_height.get("height"),
                 )
@@ -707,22 +732,12 @@ class MoonvalleyTxt2VideoNode(BaseMoonvalleyVideoNode):
 NODE_CLASS_MAPPINGS = {
     "MoonvalleyImg2VideoNode": MoonvalleyImg2VideoNode,
     "MoonvalleyTxt2VideoNode": MoonvalleyTxt2VideoNode,
-    # "MoonvalleyVideo2VideoNode": MoonvalleyVideo2VideoNode,
+    "MoonvalleyVideo2VideoNode": MoonvalleyVideo2VideoNode,
 }
 
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "MoonvalleyImg2VideoNode": "Moonvalley Marey Image to Video",
     "MoonvalleyTxt2VideoNode": "Moonvalley Marey Text to Video",
-    # "MoonvalleyVideo2VideoNode": "Moonvalley Marey Video to Video",
+    "MoonvalleyVideo2VideoNode": "Moonvalley Marey Video to Video",
 }
-
-
-def get_total_frames_from_length(length="5s"):
-    # if length == '5s':
-    #     return 128
-    # elif length == '10s':
-    #     return 256
-    return 128
-    # else:
-    #     raise MoonvalleyApiError("length is required")

comfy_extras/nodes_wan.py

@@ -685,6 +685,49 @@ class WanTrackToVideo:
         out_latent["samples"] = latent
         return (positive, negative, out_latent)
 
+
+class Wan22ImageToVideoLatent:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"vae": ("VAE", ),
+                             "width": ("INT", {"default": 1280, "min": 32, "max": nodes.MAX_RESOLUTION, "step": 32}),
+                             "height": ("INT", {"default": 704, "min": 32, "max": nodes.MAX_RESOLUTION, "step": 32}),
+                             "length": ("INT", {"default": 49, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
+                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+                },
+                "optional": {"start_image": ("IMAGE", ),
+                }}
+
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/inpaint"
+
+    def encode(self, vae, width, height, length, batch_size, start_image=None):
+        latent = torch.zeros([1, 48, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device())
+
+        if start_image is None:
+            out_latent = {}
+            out_latent["samples"] = latent
+            return (out_latent,)
+
+        mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
+
+        if start_image is not None:
+            start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            latent_temp = vae.encode(start_image)
+            latent[:, :, :latent_temp.shape[-3]] = latent_temp
+            mask[:, :, :latent_temp.shape[-3]] *= 0.0
+
+        out_latent = {}
+        latent_format = comfy.latent_formats.Wan22()
+        latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask)
+        out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
+        out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
+        return (out_latent,)
+
+
 NODE_CLASS_MAPPINGS = {
     "WanTrackToVideo": WanTrackToVideo,
     "WanImageToVideo": WanImageToVideo,
@@ -695,4 +738,5 @@ NODE_CLASS_MAPPINGS = {
     "TrimVideoLatent": TrimVideoLatent,
     "WanCameraImageToVideo": WanCameraImageToVideo,
     "WanPhantomSubjectToVideo": WanPhantomSubjectToVideo,
+    "Wan22ImageToVideoLatent": Wan22ImageToVideoLatent,
 }

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.45"
+__version__ = "0.3.46"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.45"
+version = "0.3.46"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.23.4
-comfyui-workflow-templates==0.1.40
+comfyui-workflow-templates==0.1.41
 comfyui-embedded-docs==0.2.4
 torch
 torchsde