Merge ad04a6199e into 6978a466b8

comfy/latent_formats.py

@@ -779,6 +779,9 @@ class ACEAudio(LatentFormat):
     latent_channels = 8
     latent_dimensions = 2
 
+class SeedVR2(LatentFormat):
+    latent_channels = 16
+
 class ACEAudio15(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1

comfy/ldm/modules/diffusionmodules/model.py

@@ -22,7 +22,7 @@ def torch_cat_if_needed(xl, dim):
     else:
         return None
 
-def get_timestep_embedding(timesteps, embedding_dim):
+def get_timestep_embedding(timesteps, embedding_dim, flip_sin_to_cos=False, downscale_freq_shift=1):
     """
     This matches the implementation in Denoising Diffusion Probabilistic Models:
     From Fairseq.
@@ -33,11 +33,13 @@ def get_timestep_embedding(timesteps, embedding_dim):
     assert len(timesteps.shape) == 1
 
     half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - 1)
+    emb = math.log(10000) / (half_dim - downscale_freq_shift)
     emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
     emb = emb.to(device=timesteps.device)
     emb = timesteps.float()[:, None] * emb[None, :]
     emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
     if embedding_dim % 2 == 1:  # zero pad
         emb = torch.nn.functional.pad(emb, (0,1,0,0))
     return emb

comfy/ldm/seedvr/attention.py

@@ -0,0 +1,77 @@
+import torch
+
+from comfy.ldm.modules import attention as _attention
+
+
+def _var_attention_qkv(q, k, v, heads, skip_reshape):
+    if skip_reshape:
+        return q, k, v, q.shape[-1]
+    total_tokens, embed_dim = q.shape
+    head_dim = embed_dim // heads
+    return (
+        q.view(total_tokens, heads, head_dim),
+        k.view(k.shape[0], heads, head_dim),
+        v.view(v.shape[0], heads, head_dim),
+        head_dim,
+    )
+
+
+def _var_attention_output(out, heads, head_dim, skip_output_reshape):
+    if skip_output_reshape:
+        return out
+    return out.reshape(-1, heads * head_dim)
+
+
+def _validate_split_cu_seqlens(name, cu_seqlens, token_count):
+    if cu_seqlens.dtype not in (torch.int32, torch.int64):
+        raise ValueError(f"{name} must use an integer dtype")
+    if cu_seqlens.ndim != 1 or cu_seqlens.numel() < 2:
+        raise ValueError(f"{name} must be a 1D tensor with at least two offsets")
+    if cu_seqlens[0].item() != 0:
+        raise ValueError(f"{name} must start at 0")
+    if (cu_seqlens[1:] <= cu_seqlens[:-1]).any().item():
+        raise ValueError(f"{name} must be strictly increasing")
+    if cu_seqlens[-1].item() != token_count:
+        raise ValueError(f"{name} does not match token count")
+
+
+def _split_indices(cu_seqlens):
+    return cu_seqlens[1:-1].to(device="cpu", dtype=torch.long)
+
+
+def var_attention_optimized_split(q, k, v, heads, cu_seqlens_q, cu_seqlens_k, *args, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    q, k, v, head_dim = _var_attention_qkv(q, k, v, heads, skip_reshape)
+
+    _validate_split_cu_seqlens("cu_seqlens_q", cu_seqlens_q, q.shape[0])
+    _validate_split_cu_seqlens("cu_seqlens_k", cu_seqlens_k, k.shape[0])
+    if cu_seqlens_k[-1].item() != v.shape[0]:
+        raise ValueError("cu_seqlens_k does not match v token count")
+
+    q_split_indices = _split_indices(cu_seqlens_q)
+    k_split_indices = _split_indices(cu_seqlens_k)
+    q_splits = torch.tensor_split(q, q_split_indices, dim=0)
+    k_splits = torch.tensor_split(k, k_split_indices, dim=0)
+    v_splits = torch.tensor_split(v, k_split_indices, dim=0)
+    if len(q_splits) != len(k_splits) or len(q_splits) != len(v_splits):
+        raise ValueError("cu_seqlens_q and cu_seqlens_k must describe the same sequence count")
+
+    out = []
+    for q_i, k_i, v_i in zip(q_splits, k_splits, v_splits):
+        q_i = q_i.permute(1, 0, 2).unsqueeze(0)
+        k_i = k_i.permute(1, 0, 2).unsqueeze(0)
+        v_i = v_i.permute(1, 0, 2).unsqueeze(0)
+        out_dtype = q_i.dtype
+        if _attention.optimized_attention is _attention.attention_sage and q_i.dtype not in (torch.float16, torch.bfloat16):
+            q_i = q_i.to(torch.bfloat16)
+            k_i = k_i.to(torch.bfloat16)
+            v_i = v_i.to(torch.bfloat16)
+        out_i = _attention.optimized_attention(q_i, k_i, v_i, heads, skip_reshape=True, skip_output_reshape=True)
+        if out_i.dtype != out_dtype:
+            out_i = out_i.to(out_dtype)
+        out.append(out_i.squeeze(0).permute(1, 0, 2))
+
+    out = torch.cat(out, dim=0)
+    return _var_attention_output(out, heads, head_dim, skip_output_reshape)
+
+
+optimized_var_attention = var_attention_optimized_split

comfy/ldm/seedvr/color_fix.py

@@ -0,0 +1,340 @@
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+
+from comfy.ldm.seedvr.model import safe_pad_operation
+from comfy.ldm.seedvr.vae import safe_interpolate_operation
+from comfy.ldm.seedvr.constants import (
+    CIELAB_DELTA,
+    CIELAB_KAPPA,
+    D65_WHITE_X,
+    D65_WHITE_Z,
+    WAVELET_DECOMP_LEVELS,
+)
+
+
+def wavelet_blur(image: Tensor, radius):
+    max_safe_radius = max(1, min(image.shape[-2:]) // 8)
+    if radius > max_safe_radius:
+        radius = max_safe_radius
+
+    num_channels = image.shape[1]
+
+    kernel_vals = [
+        [0.0625, 0.125, 0.0625],
+        [0.125,  0.25,  0.125],
+        [0.0625, 0.125, 0.0625],
+    ]
+    kernel = torch.tensor(kernel_vals, dtype=image.dtype, device=image.device)
+    kernel = kernel[None, None].repeat(num_channels, 1, 1, 1)
+
+    image = safe_pad_operation(image, (radius, radius, radius, radius), mode='replicate')
+    output = F.conv2d(image, kernel, groups=num_channels, dilation=radius)
+
+    return output
+
+def wavelet_decomposition(image: Tensor, levels: int = WAVELET_DECOMP_LEVELS):
+    high_freq = torch.zeros_like(image)
+
+    for i in range(levels):
+        radius = 2 ** i
+        low_freq = wavelet_blur(image, radius)
+        high_freq.add_(image).sub_(low_freq)
+        image = low_freq
+
+    return high_freq, low_freq
+
+def wavelet_reconstruction(content_feat: Tensor, style_feat: Tensor) -> Tensor:
+
+    if content_feat.shape != style_feat.shape:
+        # Resize style to match content spatial dimensions
+        if len(content_feat.shape) >= 3:
+            # safe_interpolate_operation handles FP16 conversion automatically
+            style_feat = safe_interpolate_operation(
+                style_feat,
+                size=content_feat.shape[-2:],
+                mode='bilinear',
+                align_corners=False
+            )
+
+    # Decompose both features into frequency components
+    content_high_freq, content_low_freq = wavelet_decomposition(content_feat)
+    del content_low_freq  # Free memory immediately
+
+    style_high_freq, style_low_freq = wavelet_decomposition(style_feat)
+    del style_high_freq  # Free memory immediately
+
+    if content_high_freq.shape != style_low_freq.shape:
+        style_low_freq = safe_interpolate_operation(
+            style_low_freq,
+            size=content_high_freq.shape[-2:],
+            mode='bilinear',
+            align_corners=False
+        )
+
+    content_high_freq.add_(style_low_freq)
+
+    return content_high_freq.clamp_(-1.0, 1.0)
+
+def _histogram_matching_channel(source: Tensor, reference: Tensor, device: torch.device) -> Tensor:
+    original_shape = source.shape
+
+    # Flatten
+    source_flat = source.flatten()
+    reference_flat = reference.flatten()
+
+    # Sort both arrays
+    source_sorted, source_indices = torch.sort(source_flat)
+    reference_sorted, _ = torch.sort(reference_flat)
+    del reference_flat
+
+    # Quantile mapping
+    n_source = len(source_sorted)
+    n_reference = len(reference_sorted)
+
+    if n_source == n_reference:
+        matched_sorted = reference_sorted
+    else:
+        # Interpolate reference to match source quantiles
+        source_quantiles = torch.linspace(0, 1, n_source, device=device)
+        ref_indices = (source_quantiles * (n_reference - 1)).long()
+        ref_indices.clamp_(0, n_reference - 1)
+        matched_sorted = reference_sorted[ref_indices]
+        del source_quantiles, ref_indices, reference_sorted
+
+    del source_sorted, source_flat
+
+    # Reconstruct using argsort (portable across CUDA/ROCm/MPS)
+    inverse_indices = torch.argsort(source_indices)
+    del source_indices
+    matched_flat = matched_sorted[inverse_indices]
+    del matched_sorted, inverse_indices
+
+    return matched_flat.reshape(original_shape)
+
+def _lab_to_rgb_batch(lab: Tensor, device: torch.device, matrix_inv: Tensor, epsilon: float, kappa: float) -> Tensor:
+    """Convert batch of CIELAB images to RGB color space."""
+    L, a, b = lab[:, 0], lab[:, 1], lab[:, 2]
+
+    # LAB to XYZ
+    fy = (L + 16.0) / 116.0
+    fx = a.div(500.0).add_(fy)
+    fz = fy - b / 200.0
+    del L, a, b
+
+    # XYZ transformation
+    x = torch.where(
+        fx > epsilon,
+        torch.pow(fx, 3.0),
+        fx.mul(116.0).sub_(16.0).div_(kappa)
+    )
+    y = torch.where(
+        fy > epsilon,
+        torch.pow(fy, 3.0),
+        fy.mul(116.0).sub_(16.0).div_(kappa)
+    )
+    z = torch.where(
+        fz > epsilon,
+        torch.pow(fz, 3.0),
+        fz.mul(116.0).sub_(16.0).div_(kappa)
+    )
+    del fx, fy, fz
+
+    # Apply D65 white point (in-place)
+    x.mul_(D65_WHITE_X)
+    # y *= 1.00000  # (no-op, skip)
+    z.mul_(D65_WHITE_Z)
+
+    xyz = torch.stack([x, y, z], dim=1)
+    del x, y, z
+
+    # Matrix multiplication: XYZ -> RGB
+    B, C, H, W = xyz.shape
+    xyz_flat = xyz.permute(0, 2, 3, 1).reshape(-1, 3)
+    del xyz
+
+    # Ensure dtype consistency for matrix multiplication
+    xyz_flat = xyz_flat.to(dtype=matrix_inv.dtype)
+    rgb_linear_flat = torch.matmul(xyz_flat, matrix_inv.T)
+    del xyz_flat
+
+    rgb_linear = rgb_linear_flat.reshape(B, H, W, 3).permute(0, 3, 1, 2)
+    del rgb_linear_flat
+
+    # Apply inverse gamma correction (delinearize)
+    mask = rgb_linear > 0.0031308
+    rgb = torch.where(
+        mask,
+        torch.pow(torch.clamp(rgb_linear, min=0.0), 1.0 / 2.4).mul_(1.055).sub_(0.055),
+        rgb_linear * 12.92
+    )
+    del mask, rgb_linear
+
+    return torch.clamp(rgb, 0.0, 1.0)
+
+def _rgb_to_lab_batch(rgb: Tensor, device: torch.device, matrix: Tensor, epsilon: float, kappa: float) -> Tensor:
+    """Convert batch of RGB images to CIELAB color space using D65 illuminant."""
+    # Apply sRGB gamma correction (linearize)
+    mask = rgb > 0.04045
+    rgb_linear = torch.where(
+        mask,
+        torch.pow((rgb + 0.055) / 1.055, 2.4),
+        rgb / 12.92
+    )
+    del mask
+
+    # Matrix multiplication: RGB -> XYZ
+    B, C, H, W = rgb_linear.shape
+    rgb_flat = rgb_linear.permute(0, 2, 3, 1).reshape(-1, 3)
+    del rgb_linear
+
+    # Ensure dtype consistency for matrix multiplication
+    rgb_flat = rgb_flat.to(dtype=matrix.dtype)
+    xyz_flat = torch.matmul(rgb_flat, matrix.T)
+    del rgb_flat
+
+    xyz = xyz_flat.reshape(B, H, W, 3).permute(0, 3, 1, 2)
+    del xyz_flat
+
+    # Normalize by D65 white point (in-place)
+    xyz[:, 0].div_(D65_WHITE_X)  # X
+    # xyz[:, 1] /= 1.00000  # Y (no-op, skip)
+    xyz[:, 2].div_(D65_WHITE_Z)  # Z
+
+    # XYZ to LAB transformation
+    epsilon_cubed = epsilon ** 3
+    mask = xyz > epsilon_cubed
+    f_xyz = torch.where(
+        mask,
+        torch.pow(xyz, 1.0 / 3.0),
+        xyz.mul(kappa).add_(16.0).div_(116.0)
+    )
+    del xyz, mask
+
+    # Extract channels and compute LAB
+    L = f_xyz[:, 1].mul(116.0).sub_(16.0)  # Lightness [0, 100]
+    a = (f_xyz[:, 0] - f_xyz[:, 1]).mul_(500.0)  # Green-Red [-128, 127]
+    b = (f_xyz[:, 1] - f_xyz[:, 2]).mul_(200.0)  # Blue-Yellow [-128, 127]
+    del f_xyz
+
+    return torch.stack([L, a, b], dim=1)
+
+def lab_color_transfer(
+    content_feat: Tensor,
+    style_feat: Tensor,
+    luminance_weight: float = 0.8
+) -> Tensor:
+    content_feat = wavelet_reconstruction(content_feat, style_feat)
+
+    if content_feat.shape != style_feat.shape:
+        style_feat = safe_interpolate_operation(
+            style_feat,
+            size=content_feat.shape[-2:],
+            mode='bilinear',
+            align_corners=False
+        )
+
+    device = content_feat.device
+
+    def ensure_float32_precision(c):
+        orig_dtype = c.dtype
+        c = c.float()
+        return c, orig_dtype
+    content_feat, original_dtype = ensure_float32_precision(content_feat)
+    style_feat, _ = ensure_float32_precision(style_feat)
+
+    rgb_to_xyz_matrix = torch.tensor([
+        [0.4124564, 0.3575761, 0.1804375],
+        [0.2126729, 0.7151522, 0.0721750],
+        [0.0193339, 0.1191920, 0.9503041]
+    ], dtype=torch.float32, device=device)
+
+    xyz_to_rgb_matrix = torch.tensor([
+        [ 3.2404542, -1.5371385, -0.4985314],
+        [-0.9692660,  1.8760108,  0.0415560],
+        [ 0.0556434, -0.2040259,  1.0572252]
+    ], dtype=torch.float32, device=device)
+
+    epsilon = CIELAB_DELTA
+    kappa = CIELAB_KAPPA
+
+    content_feat.add_(1.0).mul_(0.5).clamp_(0.0, 1.0)
+    style_feat.add_(1.0).mul_(0.5).clamp_(0.0, 1.0)
+
+    # Convert to LAB color space
+    content_lab = _rgb_to_lab_batch(content_feat, device, rgb_to_xyz_matrix, epsilon, kappa)
+    del content_feat
+
+    style_lab = _rgb_to_lab_batch(style_feat, device, rgb_to_xyz_matrix, epsilon, kappa)
+    del style_feat, rgb_to_xyz_matrix
+
+    # Match chrominance channels (a*, b*) for accurate color transfer
+    matched_a = _histogram_matching_channel(content_lab[:, 1], style_lab[:, 1], device)
+    matched_b = _histogram_matching_channel(content_lab[:, 2], style_lab[:, 2], device)
+
+    # Handle luminance with weighted blending
+    if luminance_weight < 1.0:
+        # Partially match luminance for better overall color accuracy
+        matched_L = _histogram_matching_channel(content_lab[:, 0], style_lab[:, 0], device)
+        # Blend: preserve some content L* for detail, adopt some style L* for color
+        result_L = content_lab[:, 0].mul(luminance_weight).add_(matched_L.mul(1.0 - luminance_weight))
+        del matched_L
+    else:
+        # Fully preserve content luminance
+        result_L = content_lab[:, 0]
+
+    del content_lab, style_lab
+
+    # Reconstruct LAB with corrected channels
+    result_lab = torch.stack([result_L, matched_a, matched_b], dim=1)
+    del result_L, matched_a, matched_b
+
+    # Convert back to RGB
+    result_rgb = _lab_to_rgb_batch(result_lab, device, xyz_to_rgb_matrix, epsilon, kappa)
+    del result_lab, xyz_to_rgb_matrix
+
+    # Convert back to [-1, 1] range (in-place)
+    result = result_rgb.mul_(2.0).sub_(1.0)
+    del result_rgb
+
+    result = result.to(original_dtype)
+
+    return result
+
+
+def wavelet_color_transfer(content_feat: Tensor, style_feat: Tensor) -> Tensor:
+    return wavelet_reconstruction(content_feat, style_feat)
+
+
+def adain_color_transfer(content_feat: Tensor, style_feat: Tensor, eps: float = 1e-5) -> Tensor:
+    if content_feat.shape != style_feat.shape:
+        style_feat = safe_interpolate_operation(
+            style_feat,
+            size=content_feat.shape[-2:],
+            mode='bilinear',
+            align_corners=False,
+        )
+
+    original_dtype = content_feat.dtype
+    content_feat = content_feat.float()
+    style_feat = style_feat.float()
+
+    b, c = content_feat.shape[:2]
+    content_flat = content_feat.reshape(b, c, -1)
+    style_flat = style_feat.reshape(b, c, -1)
+
+    content_mean = content_flat.mean(dim=2).reshape(b, c, 1, 1)
+    content_std = (content_flat.var(dim=2, correction=0) + eps).sqrt().reshape(b, c, 1, 1)
+    style_mean = style_flat.mean(dim=2).reshape(b, c, 1, 1)
+    style_std = (style_flat.var(dim=2, correction=0) + eps).sqrt().reshape(b, c, 1, 1)
+    del content_flat, style_flat
+
+    normalized = (content_feat - content_mean) / content_std
+    del content_mean, content_std
+    result = normalized * style_std + style_mean
+    del normalized, style_mean, style_std
+
+    result = result.clamp_(-1.0, 1.0)
+    if result.dtype != original_dtype:
+        result = result.to(original_dtype)
+    return result

comfy/ldm/seedvr/constants.py

@@ -0,0 +1,72 @@
+"""Named constants for the SeedVR2 integration, grouped by provenance.
+
+Provenance prefixes:
+- ``SEEDVR2_*``   - introduced by this integration (no external origin); rationale inline.
+- ``BYTEDANCE_*`` - ported from the official ByteDance-Seed/SeedVR release; each cites
+                    the upstream config/source path it was lifted from.
+- unprefixed standards (``ROPE_THETA``, ``CIELAB_*``, ``D65_*``) - published literature /
+                    ISO / CIE values; cite the standard.
+"""
+
+# --------------------------------------------------------------------------------------
+# A. Progressive-sampler chunk-size law  (SEEDVR2 - this integration's VRAM experiment)
+#    n_max(frames/chunk) = SEEDVR2_CHUNK_FRAMES_PER_GB * (free_GB - SEEDVR2_CHUNK_GB_MARGIN)
+#    rounded to the 4n+1 grid. Fit on 22 blocked-5090 cells, validated on a real RTX 4070
+#    (3b and 7b). Resolution-independent (the VAE tiling sets the wall, not the DiT).
+# --------------------------------------------------------------------------------------
+SEEDVR2_CHUNK_GB_MARGIN = 3        # fixed VRAM overhead before chunks scale (GiB)
+SEEDVR2_CHUNK_FRAMES_PER_GB = 4    # empirical slope: pixel frames admitted per free GiB
+
+# --------------------------------------------------------------------------------------
+# B. Fork heuristics  (SEEDVR2 - this integration)
+# --------------------------------------------------------------------------------------
+SEEDVR2_7B_VID_DIM = 3072          # runtime 3b-vs-7b sentinel; tested against vid_dim.
+                                   # (3072 is ByteDance's 7b vid_dim; the sentinel use is ours.)
+SEEDVR2_OOM_BACKOFF_DIVISOR = 2    # auto-chunk OOM retry: halve the chunk and retry.
+SEEDVR2_DTYPE_BYTES_FLOOR = 4      # per-element byte floor for memory math (fp32 worst case).
+SEEDVR2_7B_MLP_CHUNK = 8192        # 7b MLP token-chunk to bound peak VRAM.
+SEEDVR2_ROPE_PARTIAL_CHUNK_TOKENS = 4096  # partial-RoPE application token-chunk.
+SEEDVR2_LATENT_CHANNELS = 16       # SeedVR2 latent channel count (== BYTEDANCE latent_channels).
+SEEDVR2_COND_CHANNELS = 17         # conditioning channels = vid_in_channels(33) - latent(16).
+
+# Color-correction memory model (fork tuning; per-frame VRAM estimate for chunk sizing)
+SEEDVR2_COLOR_MEM_HEADROOM = 0.75  # fraction of free VRAM usable per color-correction chunk.
+SEEDVR2_LAB_SCALE_MULTIPLIER = 13  # per-frame byte multiplier, LAB path.
+SEEDVR2_WAVELET_SCALE_MULTIPLIER = 10  # per-frame byte multiplier, wavelet path.
+SEEDVR2_ADAIN_SCALE_MULTIPLIER = 6     # per-frame byte multiplier, AdaIN path.
+
+# --------------------------------------------------------------------------------------
+# C. ByteDance config / source  (BYTEDANCE - cite ByteDance-Seed/SeedVR)
+# --------------------------------------------------------------------------------------
+BYTEDANCE_VAE_SCALING_FACTOR = 0.9152   # configs_3b/main.yaml:57 (scaling_factor); latent denorm.
+BYTEDANCE_VAE_SHIFTING_FACTOR = 0.0     # infer.py (shifting_factor default); latent denorm shift.
+BYTEDANCE_VAE_CONV_MEM_GIB = 0.5        # configs_3b/main.yaml:54 (conv_max_mem).
+BYTEDANCE_VAE_NORM_MEM_GIB = 0.5        # configs_3b/main.yaml:55 (norm_max_mem).
+BYTEDANCE_LOGVAR_CLAMP_MIN = -30.0      # video_vae_v3/modules/types.py:28.
+BYTEDANCE_LOGVAR_CLAMP_MAX = 20.0       # video_vae_v3/modules/types.py:28.
+BYTEDANCE_GN_CHUNKS_FP16 = 4            # causal_inflation_lib.py:351 (GroupNorm chunk count, fp16).
+BYTEDANCE_GN_CHUNKS_FP32 = 2            # causal_inflation_lib.py:351 (GroupNorm chunk count, fp32).
+BYTEDANCE_BLOCK_OUT_CHANNELS = (128, 256, 512, 512)  # s8_c16_t4_inflation_sd3.yaml:7-11.
+BYTEDANCE_SLICING_SAMPLE_MIN = 4        # s8_c16_t4_inflation_sd3.yaml:22 (slicing_sample_min_size).
+BYTEDANCE_VAE_TEMPORAL_DOWNSAMPLE = 4   # infer.py:230 (temporal_downsample_factor); the 4n+1 factor.
+BYTEDANCE_VAE_SPATIAL_DOWNSAMPLE = 8    # infer.py:231 (spatial_downsample_factor).
+BYTEDANCE_720P_REF_AREA = 45 * 80       # dit_v2/window.py:32 (720p reference area for window scaling).
+BYTEDANCE_MAX_TEMPORAL_WINDOW = 30      # dit_v2/window.py:35 (max temporal window frames).
+BYTEDANCE_ROPE_MAX_FREQ = 256           # dit_v2/rope.py:31 (pixel-RoPE max frequency).
+BYTEDANCE_SINUSOIDAL_DIM = 256          # dit_3b/nadit.py:120 (timestep sinusoidal embed dim).
+
+# --------------------------------------------------------------------------------------
+# D. Published standards (cite the literature)
+# --------------------------------------------------------------------------------------
+ROPE_THETA = 10000   # RoPE base; Su et al., "RoFormer", arXiv:2104.09864.
+
+# CIELAB f(t) piecewise constants and D65 white point (CIE 15 colorimetry; CIE D65).
+CIELAB_DELTA = 6.0 / 29.0          # CIE 15 (delta).
+CIELAB_KAPPA = (29.0 / 3.0) ** 3   # CIE 15 (kappa).
+D65_WHITE_X = 0.95047              # CIE D65 standard illuminant Xn (Yn = 1).
+D65_WHITE_Z = 1.08883              # CIE D65 standard illuminant Zn.
+WAVELET_DECOMP_LEVELS = 5          # wavelet color-fix decomposition depth (GIMP/Krita; StableSR).
+
+# NOTE: the sRGB<->XYZ D65 3x3 matrices (IEC 61966-2-1) remain inline in the color code and
+# are named (SRGB_TO_XYZ_D65 / XYZ_TO_SRGB_D65) during the color-module extraction, where the
+# exact existing coefficients move verbatim rather than being retyped here.

comfy/model_base.py

@@ -55,6 +55,7 @@ import comfy.ldm.pixeldit.model
 import comfy.ldm.pixeldit.pid
 import comfy.ldm.ace.model
 import comfy.ldm.omnigen.omnigen2
+import comfy.ldm.seedvr.model
 import comfy.ldm.boogu.model
 import comfy.ldm.qwen_image.model
 import comfy.ldm.ideogram4.model
@@ -931,6 +932,16 @@ class HunyuanDiT(BaseModel):
         out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]]))
         return out
 
+class SeedVR2(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device, comfy.ldm.seedvr.model.NaDiT)
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        condition = kwargs.get("condition", None)
+        if condition is not None:
+            out["condition"] = comfy.conds.CONDRegular(condition)
+        return out
+
 class PixArt(BaseModel):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.pixart.pixartms.PixArtMS)

comfy/model_detection.py

@@ -598,6 +598,53 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
         return dit_config
 
+    if "{}blocks.35.mlp.vid.proj_in.weight".format(key_prefix) in state_dict_keys and state_dict["{}blocks.35.mlp.vid.proj_in.weight".format(key_prefix)].shape[1] == 3072: # seedvr2 7b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 3072
+        dit_config["heads"] = 24
+        dit_config["num_layers"] = 36
+        # 7B uses non-shared MMModule layout (separate ``vid.`` / ``txt.``
+        # submodules) at EVERY block — verified by inspecting the 7B
+        # state_dict at ``blocks.31.ada.txt.attn_gate`` (txt. prefix means
+        # ``MMModule.shared_weights=False``). Native NaDiT computes
+        # per-block ``shared_weights = not (i < mm_layers)``, so to keep
+        # every block non-shared we set ``mm_layers = num_layers``.
+        # Without this, blocks at index >= mm_layers (default 10) try to
+        # load ``blocks.N.*.all.*`` keys that don't exist in the file,
+        # silently miss-load → all-black output.
+        dit_config["mm_layers"] = 36
+        dit_config["norm_eps"] = 1e-5
+        dit_config["rope_type"] = "rope3d"
+        dit_config["rope_dim"] = 64
+        dit_config["mlp_type"] = "normal"
+        return dit_config
+    elif "{}blocks.35.mlp.all.proj_in_gate.weight".format(key_prefix) in state_dict_keys: # seedvr2 7b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 3072
+        dit_config["heads"] = 24
+        dit_config["num_layers"] = 36
+        # This checkpoint layout carries shared ``all.`` MMModule keys.
+        # Preserve the historical split: the initial blocks use separate
+        # vid/txt modules, later blocks use shared modules.
+        dit_config["mm_layers"] = 10
+        dit_config["norm_eps"] = 1e-5
+        dit_config["rope_type"] = "rope3d"
+        dit_config["rope_dim"] = 64
+        dit_config["mlp_type"] = "swiglu"
+        return dit_config
+    elif "{}blocks.31.mlp.all.proj_in_gate.weight".format(key_prefix) in state_dict_keys: # seedvr2 3b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 2560
+        dit_config["heads"] = 20
+        dit_config["num_layers"] = 32
+        dit_config["norm_eps"] = 1.0e-05
+        dit_config["mlp_type"] = "swiglu"
+        dit_config["vid_out_norm"] = True
+        return dit_config
+
     if '{}head.modulation'.format(key_prefix) in state_dict_keys:  # Wan 2.1
         dit_config = {}
         dit_config["image_model"] = "wan2.1"

comfy/sd.py

@@ -1,3 +1,4 @@
+import inspect
 import json
 import torch
 from enum import Enum
@@ -16,6 +17,7 @@ import comfy.ldm.cosmos.vae
 import comfy.ldm.wan.vae
 import comfy.ldm.wan.vae2_2
 import comfy.ldm.hunyuan3d.vae
+import comfy.ldm.seedvr.vae
 import comfy.ldm.triposplat.vae
 import comfy.ldm.ace.vae.music_dcae_pipeline
 import comfy.ldm.cogvideo.vae
@@ -469,8 +471,10 @@ class CLIP:
 
 class VAE:
     def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None):
-        if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
+        is_seedvr2_vae = "decoder.up_blocks.2.upsamplers.0.upscale_conv.weight" in sd
-            sd = diffusers_convert.convert_vae_state_dict(sd)
+        if not is_seedvr2_vae and 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
+            if metadata is None or metadata.get("keep_diffusers_format") != "true":
+                sd = diffusers_convert.convert_vae_state_dict(sd)
 
         if model_management.is_amd():
             VAE_KL_MEM_RATIO = 2.73
@@ -542,6 +546,20 @@ class VAE:
                 self.first_stage_model = StageC_coder()
                 self.downscale_ratio = 32
                 self.latent_channels = 16
+            elif "decoder.up_blocks.2.upsamplers.0.upscale_conv.weight" in sd: # seedvr2
+                self.first_stage_model = comfy.ldm.seedvr.vae.VideoAutoencoderKLWrapper()
+                self.latent_channels = 16
+                self.latent_dim = 3
+                self.disable_offload = True
+                self.memory_used_decode = lambda shape, dtype: self.first_stage_model.comfy_memory_used_decode(shape)
+                self.memory_used_encode = lambda shape, dtype: (max(shape[2], 5) * shape[3] * shape[4] * 64) * model_management.dtype_size(dtype)
+                self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
+                self.downscale_index_formula = (4, 8, 8)
+                self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
+                self.upscale_index_formula = (4, 8, 8)
+                self.process_input = lambda image: image * 2.0 - 1.0
+                self.crop_input = False
             elif "decoder.conv_in.weight" in sd:
                 if sd['decoder.conv_in.weight'].shape[1] == 64:
                     ddconfig = {"block_out_channels": [128, 256, 512, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 32, "downsample_match_channel": True, "upsample_match_channel": True}
@@ -1008,6 +1026,10 @@ class VAE:
         decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, index_formulas=self.upscale_index_formula, output_device=self.output_device))
 
+    def _decode_tiled_owned(self, samples, **kwargs):
+        out = self.first_stage_model.decode_tiled(samples.to(self.vae_dtype).to(self.device), **kwargs)
+        return self.process_output(out.to(device=self.output_device, dtype=self.vae_output_dtype(), copy=True))
+
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
         steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
         steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
@@ -1044,6 +1066,11 @@ class VAE:
         encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device)
 
+    def _encode_tiled_owned(self, pixel_samples, **kwargs):
+        x = self.process_input(pixel_samples).to(self.vae_dtype).to(self.device)
+        out = self.first_stage_model.encode_tiled(x, **kwargs)
+        return out.to(device=self.output_device, dtype=self.vae_output_dtype())
+
     def decode(self, samples_in, vae_options={}):
         self.throw_exception_if_invalid()
         pixel_samples = None
@@ -1091,11 +1118,19 @@ class VAE:
                 if dims == 1 or self.extra_1d_channel is not None:
                     pixel_samples = self.decode_tiled_1d(samples_in)
                 elif dims == 2:
-                    pixel_samples = self.decode_tiled_(samples_in)
+                    if getattr(self.first_stage_model, "comfy_handles_tiling", False):
+                        tile = 256 // self.spacial_compression_decode()
+                        overlap = tile // 4
+                        pixel_samples = self._decode_tiled_owned(samples_in, tile_x=tile, tile_y=tile, overlap=overlap)
+                    else:
+                        pixel_samples = self.decode_tiled_(samples_in)
                 elif dims == 3:
                     tile = 256 // self.spacial_compression_decode()
                     overlap = tile // 4
-                    pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
+                    if getattr(self.first_stage_model, "comfy_handles_tiling", False):
+                        pixel_samples = self._decode_tiled_owned(samples_in, tile_x=tile, tile_y=tile, overlap=overlap)
+                    else:
+                        pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
 
         pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
         return pixel_samples
@@ -1114,7 +1149,20 @@ class VAE:
             args["overlap"] = overlap
 
         with model_management.cuda_device_context(self.device):
-            if dims == 1 or self.extra_1d_channel is not None:
+            if getattr(self.first_stage_model, "comfy_handles_tiling", False) and dims in (2, 3):
+                tiled_args = {}
+                if tile_x is not None:
+                    tiled_args["tile_x"] = tile_x
+                if tile_y is not None:
+                    tiled_args["tile_y"] = tile_y
+                if overlap is not None:
+                    tiled_args["overlap"] = overlap
+                if tile_t is not None:
+                    tiled_args["tile_t"] = tile_t
+                if overlap_t is not None:
+                    tiled_args["overlap_t"] = overlap_t
+                output = self._decode_tiled_owned(samples, **tiled_args)
+            elif dims == 1 or self.extra_1d_channel is not None:
                 args.pop("tile_y")
                 output = self.decode_tiled_1d(samples, **args)
             elif dims == 2:
@@ -1156,6 +1204,8 @@ class VAE:
                     else:
                         pixels_in = pixels_in.to(self.device)
                         out = self.first_stage_model.encode(pixels_in)
+                    if isinstance(out, tuple):
+                        out = out[0]
                     out = out.to(self.output_device).to(dtype=self.vae_output_dtype())
                     if samples is None:
                         samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device, dtype=self.vae_output_dtype())
@@ -1175,12 +1225,18 @@ class VAE:
                 if self.latent_dim == 3:
                     tile = 256
                     overlap = tile // 4
-                    samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
+                    if getattr(self.first_stage_model, "comfy_handles_tiling", False):
+                        samples = self._encode_tiled_owned(pixel_samples, tile_x=tile, tile_y=tile, overlap=overlap)
+                    else:
+                        samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
                 elif self.latent_dim == 1 or self.extra_1d_channel is not None:
                     samples = self.encode_tiled_1d(pixel_samples)
                 else:
                     samples = self.encode_tiled_(pixel_samples)
 
+        formatter = getattr(self.first_stage_model, "comfy_format_encoded", None)
+        if formatter is not None:
+            samples = formatter(samples)
         return samples
 
     def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
@@ -1188,7 +1244,7 @@ class VAE:
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
         dims = self.latent_dim
         pixel_samples = pixel_samples.movedim(-1, 1)
-        if dims == 3:
+        if dims == 3 and pixel_samples.ndim < 5:
             if not self.not_video:
                 pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
             else:
@@ -1212,21 +1268,39 @@ class VAE:
             elif dims == 2:
                 samples = self.encode_tiled_(pixel_samples, **args)
             elif dims == 3:
-                if tile_t is not None:
+                if getattr(self.first_stage_model, "comfy_handles_tiling", False):
-                    tile_t_latent = max(2, self.downscale_ratio[0](tile_t))
+                    tiled_args = {}
+                    if tile_x is not None:
+                        tiled_args["tile_x"] = tile_x
+                    if tile_y is not None:
+                        tiled_args["tile_y"] = tile_y
+                    if overlap is not None:
+                        tiled_args["overlap"] = overlap
+                    if tile_t is not None:
+                        tiled_args["tile_t"] = tile_t
+                    if overlap_t is not None:
+                        tiled_args["overlap_t"] = overlap_t
+                    samples = self._encode_tiled_owned(pixel_samples, **tiled_args)
                 else:
-                    tile_t_latent = 9999
+                    if tile_t is not None:
-                args["tile_t"] = self.upscale_ratio[0](tile_t_latent)
+                        tile_t_latent = max(2, self.downscale_ratio[0](tile_t))
+                    else:
+                        tile_t_latent = 9999
+                    args["tile_t"] = self.upscale_ratio[0](tile_t_latent)
 
-                if overlap_t is None:
+                    spatial_overlap = overlap if overlap is not None else 64
-                    args["overlap"] = (1, overlap, overlap)
+                    if overlap_t is None:
-                else:
+                        args["overlap"] = (1, spatial_overlap, spatial_overlap)
-                    args["overlap"] = (self.upscale_ratio[0](max(1, min(tile_t_latent // 2, self.downscale_ratio[0](overlap_t)))), overlap, overlap)
+                    else:
-                maximum = pixel_samples.shape[2]
+                        args["overlap"] = (self.upscale_ratio[0](max(1, min(tile_t_latent // 2, self.downscale_ratio[0](overlap_t)))), spatial_overlap, spatial_overlap)
-                maximum = self.upscale_ratio[0](self.downscale_ratio[0](maximum))
+                    maximum = pixel_samples.shape[2]
+                    maximum = self.upscale_ratio[0](self.downscale_ratio[0](maximum))
 
-                samples = self.encode_tiled_3d(pixel_samples[:,:,:maximum], **args)
+                    samples = self.encode_tiled_3d(pixel_samples[:,:,:maximum], **args)
 
+        formatter = getattr(self.first_stage_model, "comfy_format_encoded", None)
+        if formatter is not None:
+            samples = formatter(samples)
         return samples
 
     def get_sd(self):
@@ -1777,6 +1851,17 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
 
     return (model, clip, vae)
 
+
+def _set_model_config_inference_dtype(model_config, dtype, manual_cast_dtype, device):
+    set_dtype = model_config.set_inference_dtype
+    parameters = inspect.signature(set_dtype).parameters
+    supports_device = "device" in parameters or any(p.kind == inspect.Parameter.VAR_KEYWORD for p in parameters.values())
+    if supports_device:
+        set_dtype(dtype, manual_cast_dtype, device=device)
+    else:
+        set_dtype(dtype, manual_cast_dtype)
+
+
 def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, disable_dynamic=False):
     sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True)
     out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata, disable_dynamic=disable_dynamic)
@@ -1884,7 +1969,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
         manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
     else:
         manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
-    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
+    _set_model_config_inference_dtype(model_config, unet_dtype, manual_cast_dtype, load_device)
 
     if model_config.clip_vision_prefix is not None:
         if output_clipvision:
@@ -2025,7 +2110,7 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None, disable
         manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
     else:
         manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
-    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
+    _set_model_config_inference_dtype(model_config, unet_dtype, manual_cast_dtype, load_device)
 
     if custom_operations is not None:
         model_config.custom_operations = custom_operations

comfy/supported_models.py

@@ -1684,6 +1684,35 @@ class Chroma(supported_models_base.BASE):
         t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect))
 
+class SeedVR2(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "seedvr2"
+    }
+    latent_format = comfy.latent_formats.SeedVR2
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+    sampling_settings = {
+        "shift": 1.0,
+    }
+
+    def set_inference_dtype(self, dtype, manual_cast_dtype, device=None):
+        if (
+            dtype == torch.float16
+            and manual_cast_dtype is None
+            and comfy.model_management.should_use_bf16(device)
+        ):
+            manual_cast_dtype = torch.bfloat16
+        super().set_inference_dtype(dtype, manual_cast_dtype, device=device)
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.SeedVR2(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        return None
+
 class ChromaRadiance(Chroma):
     unet_config = {
         "image_model": "chroma_radiance",
@@ -2318,6 +2347,7 @@ models = [
     HiDream,
     HiDreamO1,
     Chroma,
+    SeedVR2,
     ChromaRadiance,
     ACEStep,
     ACEStep15,

comfy/supported_models_base.py

@@ -115,7 +115,7 @@ class BASE:
         replace_prefix = {"": self.vae_key_prefix[0]}
         return utils.state_dict_prefix_replace(state_dict, replace_prefix)
 
-    def set_inference_dtype(self, dtype, manual_cast_dtype):
+    def set_inference_dtype(self, dtype, manual_cast_dtype, device=None):
         self.unet_config['dtype'] = dtype
         self.manual_cast_dtype = manual_cast_dtype
 

comfy_extras/nodes_seedvr.py

@@ -0,0 +1,997 @@
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+import torch
+import math
+import logging
+from einops import rearrange
+
+import comfy.model_management
+import comfy.sample
+import comfy.samplers
+from comfy.ldm.seedvr.color_fix import (
+    adain_color_transfer,
+    lab_color_transfer,
+    wavelet_color_transfer,
+)
+from comfy.ldm.seedvr.constants import (
+    SEEDVR2_ADAIN_SCALE_MULTIPLIER,
+    SEEDVR2_CHUNK_FRAMES_PER_GB,
+    SEEDVR2_CHUNK_GB_MARGIN,
+    SEEDVR2_COLOR_MEM_HEADROOM,
+    SEEDVR2_COND_CHANNELS,
+    SEEDVR2_DTYPE_BYTES_FLOOR,
+    SEEDVR2_LAB_SCALE_MULTIPLIER,
+    SEEDVR2_LATENT_CHANNELS,
+    SEEDVR2_OOM_BACKOFF_DIVISOR,
+    SEEDVR2_WAVELET_SCALE_MULTIPLIER,
+)
+
+from torchvision.transforms import functional as TVF
+from torchvision.transforms import Lambda
+from torchvision.transforms.functional import InterpolationMode
+
+
+_SEEDVR2_INVALID_MODEL_MSG_PREFIX = (
+    "SeedVR2Conditioning: model object does not match expected SeedVR2 structure"
+)
+
+# Private sentinel for getattr default: distinguishes "attribute missing"
+# from "attribute present but None" so the failure message is accurate.
+_ATTR_MISSING = object()
+
+
+def _seedvr2_vram_seed_frames_per_chunk(free_bytes, t_pixel):
+    """Predict the largest 4n+1 pixel-frame chunk that fits in free_bytes."""
+    free_gb = free_bytes / (1024 ** 3)
+    predicted = SEEDVR2_CHUNK_FRAMES_PER_GB * (free_gb - SEEDVR2_CHUNK_GB_MARGIN)
+    # round (not floor) to 4n+1: the fit's central prediction lands on measured n_max
+    n = round((predicted - 1) / 4)
+    seed = 4 * int(n) + 1
+    seed = max(1, min(seed, t_pixel))
+    return seed
+
+
+def _seedvr2_auto_chunk_attempts(t_latent, t_pixel, frames_per_chunk):
+    """Return stricter 4n+1 frame chunk sizes for auto OOM retries."""
+    attempts = [frames_per_chunk]
+    current_chunk_latent = (
+        t_latent if t_pixel <= frames_per_chunk
+        else (frames_per_chunk - 1) // 4 + 1
+    )
+    current_chunk_count = max(1, math.ceil(t_latent / current_chunk_latent))
+    seen = {frames_per_chunk}
+
+    for target_chunks in range(max(2, current_chunk_count + 1), t_latent + 1):
+        chunk_latent = max(1, math.ceil(t_latent / target_chunks))
+        candidate = 4 * (chunk_latent - 1) + 1
+        if candidate in seen:
+            continue
+        if candidate >= attempts[-1]:
+            continue
+        attempts.append(candidate)
+        seen.add(candidate)
+
+    return attempts
+
+
+def _resolve_seedvr2_diffusion_model(model):
+    """Resolve ``model.model.diffusion_model``, failing loud via the ``_ATTR_MISSING`` sentinel so each of the four modes (model/diffusion_model missing vs None) gives an accurate message."""
+    inner = getattr(model, "model", _ATTR_MISSING)
+    if inner is _ATTR_MISSING:
+        raise RuntimeError(
+            f"{_SEEDVR2_INVALID_MODEL_MSG_PREFIX}: input has no 'model' attribute "
+            f"(got type {type(model).__name__})."
+        )
+    if inner is None:
+        raise RuntimeError(
+            f"{_SEEDVR2_INVALID_MODEL_MSG_PREFIX}: input.model is None "
+            f"(input type {type(model).__name__})."
+        )
+    diffusion_model = getattr(inner, "diffusion_model", _ATTR_MISSING)
+    if diffusion_model is _ATTR_MISSING:
+        raise RuntimeError(
+            f"{_SEEDVR2_INVALID_MODEL_MSG_PREFIX}: 'model.model' has no "
+            f"'diffusion_model' attribute (got type {type(inner).__name__})."
+        )
+    if diffusion_model is None:
+        raise RuntimeError(
+            f"{_SEEDVR2_INVALID_MODEL_MSG_PREFIX}: 'model.model.diffusion_model' "
+            f"is None (model.model type {type(inner).__name__})."
+        )
+    return diffusion_model
+
+
+def _apply_rope_freqs_float32_cast(diffusion_model):
+    """Cast every module's ``rope.freqs`` to float32; the per-tensor dtype check (not a sentinel attr) self-corrects across Comfy's unload/reload, which would otherwise restore the archived fp16/bf16 dtype."""
+    for module in diffusion_model.modules():
+        if hasattr(module, 'rope') and hasattr(module.rope, 'freqs'):
+            if module.rope.freqs.data.dtype != torch.float32:
+                module.rope.freqs.data = module.rope.freqs.data.to(torch.float32)
+
+
+def get_conditions(latent, latent_blur):
+    t, h, w, c = latent.shape
+    cond = torch.ones([t, h, w, c + 1], device=latent.device, dtype=latent.dtype)
+    cond[:, ..., :-1] = latent_blur[:]
+    cond[:, ..., -1:] = 1.0
+    return cond
+
+def div_pad(image, factor):
+
+    height_factor, width_factor = factor
+    height, width = image.shape[-2:]
+
+    pad_height = (height_factor - (height % height_factor)) % height_factor
+    pad_width = (width_factor - (width % width_factor)) % width_factor
+
+    if pad_height == 0 and pad_width == 0:
+        return image
+
+    if isinstance(image, torch.Tensor):
+        padding = (0, pad_width, 0, pad_height)
+        image = torch.nn.functional.pad(image, padding, mode='constant', value=0.0)
+
+    return image
+
+def cut_videos(videos):
+    t = videos.size(1)
+    if t == 1:
+        return videos
+    if t <= 4 :
+        padding = [videos[:, -1].unsqueeze(1)] * (4 - t + 1)
+        padding = torch.cat(padding, dim=1)
+        videos = torch.cat([videos, padding], dim=1)
+        return videos
+    if (t - 1) % (4) == 0:
+        return videos
+    else:
+        padding = [videos[:, -1].unsqueeze(1)] * (
+            4 - ((t - 1) % (4))
+        )
+        padding = torch.cat(padding, dim=1)
+        videos = torch.cat([videos, padding], dim=1)
+        assert (videos.size(1) - 1) % (4) == 0
+        return videos
+
+def _seedvr2_input_shorter_edge(images, node_name):
+    if images.dim() == 4:
+        return min(images.shape[1], images.shape[2])
+    if images.dim() == 5:
+        return min(images.shape[2], images.shape[3])
+    raise ValueError(
+        f"{node_name}: expected 4-D or 5-D IMAGE tensor, "
+        f"got shape {tuple(images.shape)}"
+    )
+
+
+def _seedvr2_pad(images, upscaled_shorter_edge, node_name):
+    if upscaled_shorter_edge < 2:
+        raise ValueError(
+            f"{node_name}: input shorter edge must be at least 2 pixels; "
+            f"got {upscaled_shorter_edge}."
+        )
+    if images.shape[-1] > 3:
+        images = images[..., :3]
+    if images.dim() == 4:
+        # Comfy video components arrive as a 4-D IMAGE frame sequence:
+        # (frames, H, W, C). SeedVR2 consumes that as one video.
+        images = images.unsqueeze(0)
+    elif images.dim() != 5:
+        raise ValueError(
+            f"{node_name}: expected 4-D or 5-D IMAGE tensor, "
+            f"got shape {tuple(images.shape)}"
+        )
+    images = images.permute(0, 1, 4, 2, 3)
+
+    b, t, c, h, w = images.shape
+    images = images.reshape(b * t, c, h, w)
+
+    clip = Lambda(lambda x: torch.clamp(x, 0.0, 1.0))
+    images = clip(images)
+    images = div_pad(images, (16, 16))
+    _, _, new_h, new_w = images.shape
+
+    images = images.reshape(b, t, c, new_h, new_w)
+    images = cut_videos(images)
+    images_bthwc = rearrange(images, "b t c h w -> b t h w c")
+
+    return io.NodeOutput(images_bthwc)
+
+
+class SeedVR2Preprocess(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SeedVR2Preprocess",
+            display_name="Pre-Process SeedVR2 Input",
+            category="image/upscaling",
+            description="Pad a resized image for SeedVR2 model. Alpha channel is dropped. The node Post-Process SeedVR2 Output re-applies it from the original resized image.",
+            search_aliases=["seedvr2", "upscale", "video upscale", "pad", "preprocess"],
+            inputs=[
+                io.Image.Input("resized_images", tooltip="The resized image to process."),
+            ],
+            outputs=[
+                io.Image.Output("images", tooltip="The padded image for VAE encoding."),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, resized_images):
+        upscaled_shorter_edge = _seedvr2_input_shorter_edge(resized_images, "SeedVR2Preprocess")
+        return _seedvr2_pad(
+            resized_images, upscaled_shorter_edge, "SeedVR2Preprocess",
+        )
+
+
+class SeedVR2PostProcessing(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SeedVR2PostProcessing",
+            display_name="Post-Process SeedVR2 Output",
+            category="image/upscaling",
+            description="Align the generated image with the original resized image and apply color correction.",
+            search_aliases=["seedvr2", "upscale", "color correction", "color match", "postprocess"],
+            inputs=[
+                io.Image.Input("images", tooltip="The generated image to process."),
+                io.Image.Input("original_resized_images", tooltip="The original resized image before pre-processing, used as reference."),
+                io.Combo.Input("color_correction_method", options=["lab", "wavelet", "adain", "none"], default="lab", tooltip="Method to match the generated image colors to the original image. lab: transfer color in CIELAB space, preserving detail (most faithful). wavelet: transfer low-frequency color, keeping upscaled high-frequency detail. adain: match per-channel mean/std (fastest, global tint). none: skip color transfer (geometry alignment only)."),
+            ],
+            outputs=[io.Image.Output(display_name="images", tooltip="The aligned, color-corrected image.")],
+        )
+
+    @classmethod
+    def execute(cls, images, original_resized_images, color_correction_method):
+        alpha_input = None
+        if original_resized_images.shape[-1] == 4:
+            alpha_input = original_resized_images[..., 3:4]
+            original_resized_images = original_resized_images[..., :3]
+        decoded_5d, decoded_was_4d = cls._as_bthwc(images)
+        reference_full, _ = cls._as_bthwc(original_resized_images)
+        decoded_5d = cls._restore_reference_batch_time(decoded_5d, reference_full)
+
+        b = min(decoded_5d.shape[0], reference_full.shape[0])
+        t = min(decoded_5d.shape[1], reference_full.shape[1])
+        reference_h = reference_full.shape[2]
+        reference_w = reference_full.shape[3]
+
+        decoded_5d = decoded_5d[:b, :t, :, :, :]
+        target_h = min(decoded_5d.shape[2], reference_h)
+        target_w = min(decoded_5d.shape[3], reference_w)
+        decoded_5d = decoded_5d[:, :, :target_h, :target_w, :]
+        if color_correction_method in ("lab", "wavelet", "adain"):
+            reference_5d = reference_full[:b, :t, :, :, :]
+            reference_5d = cls._resize_reference(reference_5d, target_h, target_w)
+            output_device = decoded_5d.device
+            decoded_raw = cls._to_seedvr2_raw(decoded_5d)
+            reference_raw = cls._to_seedvr2_raw(reference_5d)
+            decoded_flat = rearrange(decoded_raw, "b t h w c -> (b t) c h w")
+            reference_flat = rearrange(reference_raw, "b t h w c -> (b t) c h w")
+            output = cls._color_transfer_chunked(
+                decoded_flat, reference_flat, output_device, color_correction_method,
+            )
+            output = rearrange(output, "(b t) c h w -> b t h w c", b=b, t=t)
+            output = output.add(1.0).div(2.0).clamp(0.0, 1.0)
+        elif color_correction_method == "none":
+            output = decoded_5d
+        else:
+            raise ValueError(f"SeedVR2PostProcessing: unknown color_correction_method {color_correction_method!r}")
+
+        if alpha_input is not None:
+            alpha_5d, _ = cls._as_bthwc(alpha_input)
+            alpha_5d = alpha_5d[:output.shape[0], :output.shape[1], :output.shape[2], :output.shape[3], :]
+            output = torch.cat([output, alpha_5d.to(dtype=output.dtype, device=output.device)], dim=-1)
+        h2 = output.shape[-3] - (output.shape[-3] % 2)
+        w2 = output.shape[-2] - (output.shape[-2] % 2)
+        output = output[:, :, :h2, :w2, :]
+        if decoded_was_4d:
+            output = output.reshape(-1, output.shape[-3], output.shape[-2], output.shape[-1])
+        return io.NodeOutput(output)
+
+    @staticmethod
+    def _as_bthwc(images):
+        if images.ndim == 4:
+            return images.unsqueeze(0), True
+        if images.ndim == 5:
+            return images, False
+        raise ValueError(
+            f"SeedVR2PostProcessing: expected 4-D or 5-D IMAGE tensor, got shape {tuple(images.shape)}"
+        )
+
+    @staticmethod
+    def _restore_reference_batch_time(decoded, reference):
+        if decoded.shape[0] != 1:
+            return decoded
+        ref_b, ref_t = reference.shape[:2]
+        if ref_b < 1 or decoded.shape[1] % ref_b != 0:
+            return decoded
+        decoded_t = decoded.shape[1] // ref_b
+        if decoded_t < ref_t:
+            return decoded
+        return decoded.reshape(ref_b, decoded_t, decoded.shape[2], decoded.shape[3], decoded.shape[4])
+
+    @staticmethod
+    def _to_seedvr2_raw(images):
+        return images.mul(2.0).sub(1.0)
+
+    @staticmethod
+    def _color_transfer_on_vae_device(decoded_flat, reference_flat, output_device, transfer_fn):
+        color_device = comfy.model_management.vae_device()
+        decoded_flat = decoded_flat.to(device=color_device)
+        reference_flat = reference_flat.to(device=color_device)
+        output = transfer_fn(decoded_flat, reference_flat)
+        return output.to(device=output_device)
+
+    @staticmethod
+    def _lab_color_transfer_on_vae_device(decoded_flat, reference_flat, output_device):
+        color_device = comfy.model_management.vae_device()
+        result = None
+        for start in range(decoded_flat.shape[0]):
+            decoded_frame = decoded_flat[start:start + 1].to(device=color_device).clone()
+            reference_frame = reference_flat[start:start + 1].to(device=color_device).clone()
+            output = lab_color_transfer(decoded_frame, reference_frame).to(device=output_device)
+            if result is None:
+                result = torch.empty(
+                    (decoded_flat.shape[0],) + tuple(output.shape[1:]),
+                    device=output_device,
+                    dtype=output.dtype,
+                )
+            result[start:start + 1].copy_(output)
+        if result is None:
+            raise ValueError("SeedVR2PostProcessing: LAB color correction requires at least one frame.")
+        return result
+
+    @classmethod
+    def _color_transfer_chunked(cls, decoded_flat, reference_flat, output_device, color_correction_method):
+        chunk_size = cls._estimate_color_correction_chunk_size(decoded_flat, color_correction_method)
+        while True:
+            next_chunk_size = None
+            try:
+                return cls._run_color_transfer_chunks(
+                    decoded_flat, reference_flat, output_device, color_correction_method, chunk_size,
+                )
+            except Exception as e:
+                comfy.model_management.raise_non_oom(e)
+                if chunk_size <= 1:
+                    raise RuntimeError(
+                        "SeedVR2PostProcessing: color correction OOM at one frame; "
+                        f"color_correction_method={color_correction_method}, shape={tuple(decoded_flat.shape)}."
+                    ) from e
+                next_chunk_size = max(1, chunk_size // SEEDVR2_OOM_BACKOFF_DIVISOR)
+
+            comfy.model_management.soft_empty_cache()
+            chunk_size = next_chunk_size
+
+    @classmethod
+    def _run_color_transfer_chunks(cls, decoded_flat, reference_flat, output_device, color_correction_method, chunk_size):
+        result = None
+        for start in range(0, decoded_flat.shape[0], chunk_size):
+            end = min(start + chunk_size, decoded_flat.shape[0])
+            decoded_chunk = decoded_flat[start:end]
+            reference_chunk = reference_flat[start:end]
+            if color_correction_method == "lab":
+                output = cls._lab_color_transfer_on_vae_device(decoded_chunk, reference_chunk, output_device)
+            elif color_correction_method == "wavelet":
+                output = cls._color_transfer_on_vae_device(
+                    decoded_chunk, reference_chunk, output_device, wavelet_color_transfer,
+                )
+            else:
+                output = cls._color_transfer_on_vae_device(
+                    decoded_chunk, reference_chunk, output_device, adain_color_transfer,
+                )
+            if result is None:
+                result = torch.empty(
+                    (decoded_flat.shape[0],) + tuple(output.shape[1:]),
+                    device=output_device,
+                    dtype=output.dtype,
+                )
+            result[start:end].copy_(output)
+        if result is None:
+            raise ValueError("SeedVR2PostProcessing: color correction requires at least one frame.")
+        return result
+
+    @classmethod
+    def _estimate_color_correction_chunk_size(cls, decoded_flat, color_correction_method):
+        multiplier = cls._color_correction_memory_multiplier(color_correction_method)
+        frames = decoded_flat.shape[0]
+        _, channels, height, width = decoded_flat.shape
+        dtype_bytes = max(decoded_flat.element_size(), SEEDVR2_DTYPE_BYTES_FLOOR)
+        bytes_per_frame = height * width * channels * dtype_bytes * multiplier
+        if bytes_per_frame <= 0:
+            return frames
+        color_device = comfy.model_management.vae_device()
+        free_memory = comfy.model_management.get_free_memory(color_device)
+        chunk_size = int((free_memory * SEEDVR2_COLOR_MEM_HEADROOM) // bytes_per_frame)
+        return max(1, min(frames, chunk_size))
+
+    @staticmethod
+    def _color_correction_memory_multiplier(color_correction_method):
+        if color_correction_method == "lab":
+            return SEEDVR2_LAB_SCALE_MULTIPLIER
+        if color_correction_method == "wavelet":
+            return SEEDVR2_WAVELET_SCALE_MULTIPLIER
+        if color_correction_method == "adain":
+            return SEEDVR2_ADAIN_SCALE_MULTIPLIER
+        raise ValueError(f"SeedVR2PostProcessing: unknown color_correction_method {color_correction_method!r}")
+
+    @staticmethod
+    def _resize_reference(reference, height, width):
+        if reference.shape[2] == height and reference.shape[3] == width:
+            return reference
+        b, t = reference.shape[:2]
+        reference_flat = rearrange(reference, "b t h w c -> (b t) c h w")
+        resized = TVF.resize(
+            reference_flat,
+            size=(height, width),
+            interpolation=InterpolationMode.BICUBIC,
+            antialias=not (isinstance(reference_flat, torch.Tensor) and reference_flat.device.type == "mps"),
+        )
+        return rearrange(resized, "(b t) c h w -> b t h w c", b=b, t=t)
+
+
+class SeedVR2Conditioning(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SeedVR2Conditioning",
+            display_name="Apply SeedVR2 Conditioning",
+            category="conditioning",
+            description="Build SeedVR2 positive/negative conditioning from a VAE latent.",
+            search_aliases=["seedvr2", "upscale", "conditioning"],
+            inputs=[
+                io.Model.Input("model", tooltip="The SeedVR2 model."),
+                io.Latent.Input("vae_conditioning", display_name="latent"),
+            ],
+            outputs=[
+                io.Model.Output(display_name="model", tooltip="The SeedVR2 model, passed through."),
+                io.Conditioning.Output(display_name="positive", tooltip="The positive conditioning for sampling."),
+                io.Conditioning.Output(display_name="negative", tooltip="The negative conditioning for sampling."),
+                io.Latent.Output(display_name="latent", tooltip="The latent to denoise."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, vae_conditioning) -> io.NodeOutput:
+
+        vae_conditioning = vae_conditioning["samples"]
+        if vae_conditioning.ndim != 5:
+            raise ValueError(
+                "SeedVR2Conditioning expects a 5-D VAE latent in Comfy "
+                f"channel-first layout; got shape {tuple(vae_conditioning.shape)}."
+            )
+        if vae_conditioning.shape[-1] == SEEDVR2_LATENT_CHANNELS and vae_conditioning.shape[1] != SEEDVR2_LATENT_CHANNELS:
+            raise ValueError(
+                "SeedVR2Conditioning expects SeedVR2 VAE latents in Comfy "
+                f"channel-first layout (B, {SEEDVR2_LATENT_CHANNELS}, T, H, W); "
+                f"got channel-last shape {tuple(vae_conditioning.shape)}."
+            )
+        vae_conditioning = vae_conditioning.movedim(1, -1).contiguous()
+        model_patcher = model
+        model = _resolve_seedvr2_diffusion_model(model_patcher)
+        pos_cond = model.positive_conditioning
+        neg_cond = model.negative_conditioning
+
+        # Fail-loud guard against silently-wrong output when a
+        # DiT-only ``.safetensors`` (no ``positive_conditioning`` /
+        # ``negative_conditioning`` keys) is loaded via ``UNETLoader``.
+        # ``NaDiT.__init__`` zero-fills the buffers via ``torch.zeros`` (see
+        # ``comfy/ldm/seedvr/model.py``); ``load_state_dict(strict=False)``
+        # leaves them at zero when the keys are absent. Detect that state
+        # here rather than at ``BaseModel.extra_conds`` (per sampling step,
+        # wasteful) or at the resolver helper (mixes structural shape with
+        # semantic content). Both buffers must be checked together — partial
+        # bake regressions could populate one but not the other.
+        if (
+            pos_cond.float().abs().sum().item() == 0
+            and neg_cond.float().abs().sum().item() == 0
+        ):
+            raise RuntimeError(
+                f"{_SEEDVR2_INVALID_MODEL_MSG_PREFIX}: positive_conditioning "
+                f"and negative_conditioning buffers are zero-valued — model "
+                f"file appears to be a DiT-only export missing "
+                f"the SeedVR2 conditioning tensors. "
+                f"Re-bake the file with ``positive_conditioning`` (58, 5120) "
+                f"and ``negative_conditioning`` (64, 5120) keys at top level, "
+                f"or load via CheckpointLoaderSimple from a bundled "
+                f"checkpoint."
+            )
+
+        _apply_rope_freqs_float32_cast(model)
+
+        condition = torch.stack([get_conditions(c, c) for c in vae_conditioning])
+        condition = condition.movedim(-1, 1)
+        latent = vae_conditioning.movedim(-1, 1)
+
+        latent = rearrange(latent, "b c t h w -> b (c t) h w")
+        condition = rearrange(condition, "b c t h w -> b (c t) h w")
+
+        negative = [[neg_cond.unsqueeze(0), {"condition": condition}]]
+        positive = [[pos_cond.unsqueeze(0), {"condition": condition}]]
+
+        return io.NodeOutput(model_patcher, positive, negative, {"samples": latent})
+
+def _slice_collapsed_4d_along_t(tensor_4d: torch.Tensor, t_start: int,
+                                 t_end: int, channels: int) -> torch.Tensor:
+    """Slice collapsed ``(B, channels*T, H, W)`` along latent T: reshape (accepts non-contiguous inputs), slice, ``.contiguous()`` (T-slice of 5D is a non-contiguous view; re-collapse needs contiguous), re-collapse."""
+    B, CT, H, W = tensor_4d.shape
+    if CT % channels != 0:
+        raise ValueError(
+            f"_slice_collapsed_4d_along_t: collapsed channel dim {CT} is not "
+            f"divisible by channels={channels}; tensor shape {tuple(tensor_4d.shape)}."
+        )
+    T = CT // channels
+    if not (0 <= t_start < t_end <= T):
+        raise ValueError(
+            f"_slice_collapsed_4d_along_t: slice [{t_start}:{t_end}] out of "
+            f"range for T={T}."
+        )
+    new_T = t_end - t_start
+    sliced = tensor_4d.reshape(B, channels, T, H, W)[:, :, t_start:t_end, :, :].contiguous()
+    return sliced.reshape(B, channels * new_T, H, W)
+
+
+def _slice_seedvr2_cond_along_t(cond_list, t_start: int, t_end: int):
+    """Return a new conditioning list with each entry's ``options["condition"]`` (collapsed ``(B, 17*T, H, W)``) sliced along latent T; text tensors, other option keys, and condition-less entries pass through unchanged and inputs are not mutated."""
+    new_list = []
+    for entry in cond_list:
+        text_cond, options = entry[0], entry[1]
+        if "condition" not in options:
+            new_list.append(entry)
+            continue
+        new_options = options.copy()
+        new_options["condition"] = _slice_collapsed_4d_along_t(
+            new_options["condition"], t_start, t_end,
+            SEEDVR2_COND_CHANNELS,
+        )
+        new_list.append([text_cond, new_options])
+    return new_list
+
+
+def _slice_seedvr2_noise_mask_along_t(noise_mask: torch.Tensor,
+                                      samples_4d: torch.Tensor,
+                                      t_start: int,
+                                      t_end: int):
+    """Slice only masks already expanded to collapsed ``(B, 16*T, H, W)``; pass standard ``(B, 1, H, W)`` ``SetLatentNoiseMask`` outputs through for KSampler to expand."""
+    if noise_mask.ndim == samples_4d.ndim and noise_mask.shape[1] == samples_4d.shape[1]:
+        return _slice_collapsed_4d_along_t(
+            noise_mask, t_start, t_end, SEEDVR2_LATENT_CHANNELS,
+        )
+    return noise_mask
+
+
+def _concat_chunks_along_t(chunks_4d, channels: int) -> torch.Tensor:
+    """Concatenate collapsed ``(B, channels*T_i, H, W)`` chunks along latent T: un-collapse to 5D, cat on ``dim=2``, re-collapse to 4D."""
+    if len(chunks_4d) == 0:
+        raise ValueError("_concat_chunks_along_t: empty chunk list.")
+    fives = []
+    for ch in chunks_4d:
+        B, CT, H, W = ch.shape
+        if CT % channels != 0:
+            raise ValueError(
+                f"_concat_chunks_along_t: chunk shape {tuple(ch.shape)} "
+                f"channel dim {CT} not divisible by channels={channels}."
+            )
+        T = CT // channels
+        fives.append(ch.reshape(B, channels, T, H, W))
+    cat = torch.cat(fives, dim=2).contiguous()
+    B, C, T_total, H, W = cat.shape
+    return cat.reshape(B, C * T_total, H, W)
+
+
+def _hann_blend_weights_1d(overlap: int, device, dtype) -> torch.Tensor:
+    """1D length-``overlap`` crossfade weights for the previous chunk (current = ``1 - w_prev``):
+    Hann window with a ``[1/3, 2/3]`` dead-band for ``overlap >= 3``, linear ramp for ``overlap < 3``
+    (dead-band would collapse a tiny transition). Window shape matched to the reference
+    overlapping-frame blend for parity; caller broadcasts across ``(B, C, T_overlap, H, W)``.
+    """
+    if overlap < 1:
+        raise ValueError(
+            f"_hann_blend_weights_1d: overlap must be >= 1; got {overlap}."
+        )
+    if overlap >= 3:
+        t = torch.linspace(0.0, 1.0, steps=overlap, device=device, dtype=dtype)
+        blend_start = 1.0 / 3.0
+        blend_end = 2.0 / 3.0
+        u = ((t - blend_start) / (blend_end - blend_start)).clamp(0.0, 1.0)
+        return 0.5 + 0.5 * torch.cos(torch.pi * u)
+    return torch.linspace(1.0, 0.0, steps=overlap, device=device, dtype=dtype)
+
+
+def _blend_overlap_region(prev_tail_5d: torch.Tensor,
+                          cur_head_5d: torch.Tensor) -> torch.Tensor:
+    """Blend two equal-shape 5D ``(B, C, T_overlap, H, W)`` tensors with a 1D Hann/linear T-ramp: ``prev_tail_5d`` takes the descending weight, ``cur_head_5d`` takes ``1 - w_prev`` (caller ensures matching shape/dtype/device)."""
+    if prev_tail_5d.shape != cur_head_5d.shape:
+        raise ValueError(
+            f"_blend_overlap_region: shape mismatch "
+            f"prev {tuple(prev_tail_5d.shape)} vs "
+            f"cur {tuple(cur_head_5d.shape)}."
+        )
+    overlap = int(prev_tail_5d.shape[2])
+    w_prev_1d = _hann_blend_weights_1d(
+        overlap, prev_tail_5d.device, prev_tail_5d.dtype,
+    )
+    # Reshape to (1, 1, overlap, 1, 1) for broadcast across B, C, H, W.
+    w_prev = w_prev_1d.view(1, 1, overlap, 1, 1)
+    w_cur = 1.0 - w_prev
+    return prev_tail_5d * w_prev + cur_head_5d * w_cur
+
+
+def _concat_chunks_with_overlap_blend(chunk_specs, channels: int,
+                                      overlap_latent: int) -> torch.Tensor:
+    """Concatenate overlapping ``(t_start, t_end, chunk_4d)`` specs (source-latent T coords) into one collapsed 4D tensor, Hann/linear-blending overlaps; ``overlap_latent == 0`` fast-paths to plain concat (bit-identical to ``_concat_chunks_along_t``). Each blend uses the actual width ``min(prev_end - cur_start, chunk length)``, smaller than ``overlap_latent`` for a runt final chunk."""
+    if len(chunk_specs) == 0:
+        raise ValueError("_concat_chunks_with_overlap_blend: empty chunk list.")
+    if overlap_latent < 0:
+        raise ValueError(
+            f"_concat_chunks_with_overlap_blend: overlap_latent must be "
+            f">= 0; got {overlap_latent}."
+        )
+
+    # Validate channel divisibility once and capture per-chunk T.
+    chunk_5d = []
+    for t_start, t_end, ch in chunk_specs:
+        B, CT, H, W = ch.shape
+        if CT % channels != 0:
+            raise ValueError(
+                f"_concat_chunks_with_overlap_blend: chunk shape "
+                f"{tuple(ch.shape)} channel dim {CT} not divisible "
+                f"by channels={channels}."
+            )
+        T = CT // channels
+        if t_end - t_start != T:
+            raise ValueError(
+                f"_concat_chunks_with_overlap_blend: chunk T={T} mismatches "
+                f"declared range [{t_start}:{t_end}]."
+            )
+        chunk_5d.append((t_start, t_end, ch.reshape(B, channels, T, H, W)))
+
+    if overlap_latent == 0:
+        # Fast path: pure concat in the caller-provided chunk order.
+        return _concat_chunks_along_t(
+            [c.reshape(c.shape[0], channels * c.shape[2], c.shape[3], c.shape[4])
+             for _, _, c in chunk_5d],
+            channels,
+        )
+
+    T_total = max(t_end for _, t_end, _ in chunk_5d)
+    first_5d = chunk_5d[0][2]
+    B = first_5d.shape[0]
+    H = first_5d.shape[3]
+    W = first_5d.shape[4]
+    result = torch.empty(
+        (B, channels, T_total, H, W),
+        device=first_5d.device, dtype=first_5d.dtype,
+    )
+    filled_until = 0
+    for i, (cs, ce, ct_5d) in enumerate(chunk_5d):
+        chunk_T = int(ct_5d.shape[2])
+        if i == 0:
+            result[:, :, cs:ce, :, :] = ct_5d
+            filled_until = ce
+            continue
+        # Overlap region width is bounded by both the previous fill
+        # frontier and the current chunk's actual length (for runt
+        # final chunks shorter than the configured overlap).
+        overlap_len = min(filled_until - cs, chunk_T)
+        if overlap_len > 0:
+            prev_tail = result[:, :, cs:cs + overlap_len, :, :].contiguous()
+            cur_head = ct_5d[:, :, :overlap_len, :, :].contiguous()
+            blended = _blend_overlap_region(prev_tail, cur_head)
+            result[:, :, cs:cs + overlap_len, :, :] = blended
+            tail_start = cs + overlap_len
+            tail_end = ce
+            if tail_end > tail_start:
+                result[:, :, tail_start:tail_end, :, :] = (
+                    ct_5d[:, :, overlap_len:, :, :]
+                )
+        else:
+            # Disjoint chunks (overlap_latent set but this pair did not
+            # actually overlap, e.g. step_latent equal to chunk_latent
+            # in a degenerate config). Treat as concat.
+            result[:, :, cs:ce, :, :] = ct_5d
+        filled_until = ce
+
+    return result.contiguous().reshape(B, channels * T_total, H, W)
+
+
+def _run_standard_sample(model, seed: int, steps: int, cfg: float,
+                         sampler_name: str, scheduler: str,
+                         positive, negative, latent: dict,
+                         denoise: float) -> dict:
+    """Single-shot mirror of ``nodes.py:common_ksampler`` (seed -> noise, ``comfy.sample.sample``, latent dict); used by the ProgressiveSampler short-circuit when the whole sequence fits one chunk."""
+    samples_in = latent["samples"]
+    samples_in = comfy.sample.fix_empty_latent_channels(
+        model, samples_in, latent.get("downscale_ratio_spacial", None),
+    )
+    batch_inds = latent.get("batch_index", None)
+    noise = comfy.sample.prepare_noise(samples_in, seed, batch_inds)
+    noise_mask = latent.get("noise_mask", None)
+    samples = comfy.sample.sample(
+        model, noise, steps, cfg, sampler_name, scheduler,
+        positive, negative, samples_in,
+        denoise=denoise, noise_mask=noise_mask, seed=seed,
+    )
+    out = latent.copy()
+    out.pop("downscale_ratio_spacial", None)
+    out["samples"] = samples
+    return out
+
+
+class SeedVR2ProgressiveSampler(io.ComfyNode):
+    """Sequential temporal chunking sampler for SeedVR2 native.
+
+    Drop-in replacement for ``KSampler`` in SeedVR2 native workflows that
+    OOM on long sequences. The latent enters the sampler in SeedVR2's
+    collapsed form ``(B, 16*T, H, W)`` (collapsed by ``SeedVR2Conditioning``
+    at ``rearrange(b c t h w -> b (c t) h w)``); this node slices that
+    tensor along the temporal axis, runs the configured inner sampler
+    sequentially per chunk against the standard ``comfy.sample.sample``
+    entry point, and concatenates per-chunk outputs back into a single
+    ``(B, 16*T_total, H, W)`` latent.
+
+    ``frames_per_chunk`` is expressed in pixel-frame units to match the
+    SeedVR2 4n+1 constraint enforced upstream by ``cut_videos`` and the
+    VAE's ``temporal_downsample_factor=4``. A pixel chunk size ``F``
+    maps to ``(F - 1) // 4 + 1`` latent-frame chunks.
+
+    Determinism contract: a single noise tensor is generated once from
+    the user seed and sliced per chunk (rather than re-seeding each
+    chunk), so a workflow that fits in a single chunk produces output
+    identical to a workflow that fits in N chunks at the same seed,
+    modulo the inherent T-axis chunk-boundary independence of the model.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SeedVR2ProgressiveSampler",
+            display_name="Sample SeedVR2 (Progressive)",
+            category="sampling",
+            description="Sample a SeedVR2 latent in sequential temporal chunks to allow longer videos to fit into VRAM via frame blending the resulting upscaled latents.",
+            search_aliases=["seedvr2", "upscale", "video upscale", "sampler", "chunk"],
+            inputs=[
+                io.Model.Input("model", tooltip="The model used for denoising the input latent."),
+                io.Int.Input("seed", default=0, min=0,
+                             max=0xffffffffffffffff,
+                             control_after_generate=True,
+                             tooltip="The random seed used for creating the noise."),
+                io.Int.Input("steps", default=20, min=1, max=10000,
+                             tooltip="The number of steps used in the denoising process."),
+                io.Float.Input("cfg", default=1.0, min=0.0, max=100.0,
+                               step=0.1, round=0.01,
+                               tooltip="The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."),
+                io.Combo.Input("sampler_name",
+                               options=comfy.samplers.SAMPLER_NAMES,
+                               tooltip="The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."),
+                io.Combo.Input("scheduler",
+                               options=comfy.samplers.SCHEDULER_NAMES,
+                               tooltip="The scheduler controls how noise is gradually removed to form the image."),
+                io.Conditioning.Input("positive",
+                               tooltip="The conditioning describing the attributes you want to include in the image."),
+                io.Conditioning.Input("negative",
+                               tooltip="The conditioning describing the attributes you want to exclude from the image."),
+                io.Latent.Input("latent",
+                               tooltip="The latent image to denoise."),
+                io.Float.Input("denoise", default=1.0, min=0.0, max=1.0,
+                               step=0.01,
+                               tooltip="The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."),
+                io.Int.Input("frames_per_chunk", default=21, min=1,
+                             max=16384, step=4,
+                             tooltip="Pixel frames per temporal chunk (4n+1: 1, 5, 9, 13, ...)."),
+                io.Int.Input("temporal_overlap", default=0, min=0,
+                             max=16384,
+                             tooltip="Latent frames blended between adjacent chunks to hide the seam; 0 = no blend."),
+                io.Combo.Input("chunking_mode",
+                               options=["manual", "auto"],
+                               default="manual",
+                               tooltip="manual = use frames_per_chunk exactly; auto = shrink the chunk until it fits in VRAM."),
+            ],
+            outputs=[io.Latent.Output(display_name="latent", tooltip="The upscaled latent.")],
+        )
+
+    @classmethod
+    def execute(cls, model, seed, steps, cfg, sampler_name, scheduler,
+                positive, negative, latent, denoise,
+                frames_per_chunk, temporal_overlap,
+                chunking_mode="manual") -> io.NodeOutput:
+        # 4n+1 validation in pixel-frame domain. The SeedVR2 native pipeline
+        # requires pixel-frame counts of the form 4n+1 (1, 5, 9, 13, ...),
+        # imposed at ``cut_videos`` upstream and propagated through the VAE's
+        # temporal_downsample_factor=4. Reject violations explicitly before
+        # any model invocation; a silent rounding would mis-align chunk
+        # boundaries with the 4n+1 lattice.
+        if frames_per_chunk < 1 or (frames_per_chunk - 1) % 4 != 0:
+            raise ValueError(
+                f"SeedVR2ProgressiveSampler: frames_per_chunk must be a "
+                f"4n+1 pixel-frame count (1, 5, 9, 13, 17, 21, ...); "
+                f"got {frames_per_chunk}."
+            )
+
+        samples_4d = latent["samples"]
+        if torch.count_nonzero(samples_4d) == 0:
+            raise ValueError(
+                "SeedVR2ProgressiveSampler: input latent is empty (all zeros). "
+                "SeedVR2 is an upscaler; connect an encoded latent from "
+                "'Apply SeedVR2 conditioning' rather than an empty latent."
+            )
+        samples_4d = comfy.sample.fix_empty_latent_channels(
+            model, samples_4d,
+            latent.get("downscale_ratio_spacial", None),
+        )
+        if samples_4d.ndim != 4:
+            raise ValueError(
+                f"SeedVR2ProgressiveSampler: expected 4D collapsed latent "
+                f"(B, 16*T, H, W); got shape {tuple(samples_4d.shape)}."
+            )
+        B, CT, H, W = samples_4d.shape
+        if CT % SEEDVR2_LATENT_CHANNELS != 0:
+            raise ValueError(
+                f"SeedVR2ProgressiveSampler: collapsed channel dim {CT} is "
+                f"not divisible by SeedVR2 latent channels "
+                f"{SEEDVR2_LATENT_CHANNELS}; latent does not appear to be "
+                f"SeedVR2-shaped."
+            )
+        T_latent = CT // SEEDVR2_LATENT_CHANNELS
+        T_pixel = 4 * (T_latent - 1) + 1
+
+        if chunking_mode not in ("manual", "auto"):
+            raise ValueError(
+                f"SeedVR2ProgressiveSampler: chunking_mode must be "
+                f"'manual' or 'auto'; got {chunking_mode!r}."
+            )
+
+        if chunking_mode == "auto":
+            free_memory = comfy.model_management.get_free_memory(model.load_device)
+            seed_frames_per_chunk = _seedvr2_vram_seed_frames_per_chunk(
+                free_memory, T_pixel,
+            )
+            logging.info(
+                "SeedVR2ProgressiveSampler auto: free=%.2fGB -> seeding "
+                "frames_per_chunk=%s (4n+1; T_pixel=%s).",
+                free_memory / (1024 ** 3), seed_frames_per_chunk, T_pixel,
+            )
+            attempts = _seedvr2_auto_chunk_attempts(
+                T_latent, T_pixel, seed_frames_per_chunk,
+            )
+            for i, attempt_frames_per_chunk in enumerate(attempts):
+                retry = False
+                try:
+                    return cls.execute(
+                        model=model, seed=seed, steps=steps, cfg=cfg,
+                        sampler_name=sampler_name, scheduler=scheduler,
+                        positive=positive, negative=negative,
+                        latent=latent, denoise=denoise,
+                        frames_per_chunk=attempt_frames_per_chunk,
+                        temporal_overlap=temporal_overlap,
+                        chunking_mode="manual",
+                    )
+                except Exception as e:
+                    comfy.model_management.raise_non_oom(e)
+                    if i == len(attempts) - 1:
+                        raise RuntimeError(
+                            "SeedVR2ProgressiveSampler: exhausted auto "
+                            "chunking attempts after OOM. Tried "
+                            f"frames_per_chunk values {attempts}."
+                        ) from e
+                    retry = True
+
+                if retry:
+                    logging.warning(
+                        "SeedVR2ProgressiveSampler auto chunking OOM at "
+                        "frames_per_chunk=%s; retrying with "
+                        "frames_per_chunk=%s.",
+                        attempt_frames_per_chunk, attempts[i + 1],
+                    )
+                    comfy.model_management.soft_empty_cache()
+
+        # Short-circuit: total fits in one chunk -> standard path with no
+        # chunking overhead. Output of this branch is byte-identical to the
+        # built-in KSampler given the same (model, seed, steps, cfg,
+        # sampler_name, scheduler, positive, negative, latent,
+        # denoise) tuple.
+        if T_pixel <= frames_per_chunk:
+            return io.NodeOutput(_run_standard_sample(
+                model, seed, steps, cfg, sampler_name, scheduler,
+                positive, negative, latent, denoise,
+            ))
+
+        # Map pixel chunk -> latent chunk. Each chunk's latent length is
+        # at most ``chunk_latent``; the final chunk may be a runt that
+        # is automatically 4n+1-aligned in the pixel domain by the
+        # T_pixel = 4*(T_latent-1) + 1 mapping (every positive integer
+        # T_latent corresponds to a valid 4n+1 pixel count).
+        chunk_latent = (frames_per_chunk - 1) // 4 + 1
+
+        # ``temporal_overlap`` is exposed in latent-frame units, but users
+        # do not know the derived latent chunk length. Treat oversized
+        # values as "maximum valid overlap" while preserving a strictly
+        # positive chunk-loop stride.
+        if temporal_overlap < 0:
+            raise ValueError(
+                f"SeedVR2ProgressiveSampler: temporal_overlap must be >= 0; "
+                f"got {temporal_overlap}."
+            )
+        temporal_overlap = min(temporal_overlap, chunk_latent - 1)
+        step_latent = chunk_latent - temporal_overlap
+
+        # Generate full noise once from the user seed, then slice along T
+        # per chunk. Using one global noise tensor (rather than re-seeding
+        # per chunk) preserves seed-determinism across chunk-count
+        # variations: the same (seed, total T_latent) always produces the
+        # same noise samples regardless of how the work is partitioned.
+        batch_inds = latent.get("batch_index", None)
+        noise_full = comfy.sample.prepare_noise(samples_4d, seed, batch_inds)
+
+        noise_mask = latent.get("noise_mask", None)
+
+        # Build the flat list of chunk ranges first so the chunking
+        # geometry is fully known before any sample call.
+        chunk_ranges = []
+        for chunk_start in range(0, T_latent, step_latent):
+            chunk_end = min(chunk_start + chunk_latent, T_latent)
+            if chunk_start >= chunk_end:
+                # The final iteration of a stride that lands exactly on
+                # T_latent produces a zero-length chunk; skip it.
+                break
+            chunk_ranges.append((chunk_start, chunk_end))
+            if chunk_end >= T_latent:
+                break
+
+        def _sample_one_chunk(chunk_start, chunk_end):
+            samples_chunk = _slice_collapsed_4d_along_t(
+                samples_4d, chunk_start, chunk_end,
+                SEEDVR2_LATENT_CHANNELS,
+            )
+            noise_chunk = _slice_collapsed_4d_along_t(
+                noise_full, chunk_start, chunk_end,
+                SEEDVR2_LATENT_CHANNELS,
+            )
+            positive_chunk = _slice_seedvr2_cond_along_t(
+                positive, chunk_start, chunk_end,
+            )
+            negative_chunk = _slice_seedvr2_cond_along_t(
+                negative, chunk_start, chunk_end,
+            )
+
+            # Per-chunk noise_mask handling: standard masks are passed
+            # through for KSampler expansion; pre-expanded collapsed
+            # masks are sliced.
+            chunk_noise_mask = None
+            if noise_mask is not None:
+                chunk_noise_mask = _slice_seedvr2_noise_mask_along_t(
+                    noise_mask, samples_4d, chunk_start, chunk_end,
+                )
+
+            return comfy.sample.sample(
+                model, noise_chunk, steps, cfg, sampler_name, scheduler,
+                positive_chunk, negative_chunk, samples_chunk,
+                denoise=denoise, noise_mask=chunk_noise_mask, seed=seed,
+            )
+
+        chunk_specs = []
+        for chunk_start, chunk_end in chunk_ranges:
+            chunk_samples = _sample_one_chunk(chunk_start, chunk_end)
+            chunk_specs.append((chunk_start, chunk_end, chunk_samples))
+
+        final = _concat_chunks_with_overlap_blend(
+            chunk_specs, SEEDVR2_LATENT_CHANNELS, temporal_overlap,
+        )
+
+        out = latent.copy()
+        out.pop("downscale_ratio_spacial", None)
+        out["samples"] = final
+        return io.NodeOutput(out)
+
+
+class SeedVRExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SeedVR2Conditioning,
+            SeedVR2Preprocess,
+            SeedVR2PostProcessing,
+            SeedVR2ProgressiveSampler,
+        ]
+
+async def comfy_entrypoint() -> SeedVRExtension:
+    return SeedVRExtension()

nodes.py

@@ -2430,6 +2430,7 @@ async def init_builtin_extra_nodes():
         "nodes_camera_trajectory.py",
         "nodes_edit_model.py",
         "nodes_tcfg.py",
+        "nodes_seedvr.py",
         "nodes_context_windows.py",
         "nodes_qwen.py",
         "nodes_boogu.py",

tests-unit/comfy_extras_test/test_seedvr2_conditioning.py

@@ -0,0 +1,213 @@
+"""Consolidated SeedVR2 conditioning and refactor regression tests.
+
+Merges the prior test_seedvr2_refactor_nodes.py and
+test_seedvr_conditioning_hardening.py modules. Refactor tests use the
+top-level comfy_extras.nodes_seedvr import; conditioning-hardening tests
+use _import_nodes_seedvr_isolated() for sys.modules isolation when
+mocking comfy.model_management.
+"""
+
+import importlib
+import sys
+from unittest.mock import MagicMock
+
+import pytest
+import torch
+import torch.nn as nn
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+
+_SENTINEL = object()
+_TARGETS = (
+    ("comfy.model_management", "comfy"),
+    ("comfy_extras.nodes_seedvr", "comfy_extras"),
+)
+
+
+def _import_nodes_seedvr_isolated():
+    """Import comfy_extras.nodes_seedvr with comfy.model_management mocked."""
+    priors = []
+    for mod_name, parent_name in _TARGETS:
+        prior_mod = sys.modules.get(mod_name, _SENTINEL)
+        parent = sys.modules.get(parent_name)
+        attr = mod_name.split(".")[-1]
+        prior_attr = (
+            getattr(parent, attr, _SENTINEL) if parent is not None else _SENTINEL
+        )
+        priors.append((mod_name, parent_name, attr, prior_mod, prior_attr))
+
+    mock_mm = MagicMock()
+    for fn in (
+        "xformers_enabled", "xformers_enabled_vae",
+        "pytorch_attention_enabled", "pytorch_attention_enabled_vae",
+        "sage_attention_enabled", "flash_attention_enabled",
+        "is_intel_xpu",
+    ):
+        getattr(mock_mm, fn).return_value = False
+    tv = torch.version.__version__.split(".")
+    mock_mm.torch_version_numeric = (int(tv[0]), int(tv[1]))
+    mock_mm.WINDOWS = False
+    sys.modules["comfy.model_management"] = mock_mm
+    if sys.modules.get("comfy") is None:
+        import comfy as _comfy_pkg  # noqa: F401
+    comfy_pkg = sys.modules.get("comfy")
+    if comfy_pkg is not None:
+        setattr(comfy_pkg, "model_management", mock_mm)
+    nodes_seedvr = sys.modules.get("comfy_extras.nodes_seedvr") or (
+        importlib.import_module("comfy_extras.nodes_seedvr")
+    )
+
+    def _restore():
+        for mod_name, parent_name, attr, prior_mod, prior_attr in priors:
+            if prior_mod is _SENTINEL:
+                sys.modules.pop(mod_name, None)
+            else:
+                sys.modules[mod_name] = prior_mod
+            parent = sys.modules.get(parent_name)
+            if parent is None:
+                continue
+            if prior_attr is _SENTINEL:
+                if hasattr(parent, attr):
+                    delattr(parent, attr)
+            else:
+                setattr(parent, attr, prior_attr)
+
+    return nodes_seedvr, _restore
+
+
+class _Rope(nn.Module):
+    """Minimal RoPE stub exposing a `freqs` parameter."""
+    def __init__(self):
+        super().__init__()
+        self.freqs = nn.Parameter(torch.zeros(4))
+
+
+class _Block(nn.Module):
+    """Minimal transformer block stub holding a `_Rope`."""
+    def __init__(self):
+        super().__init__()
+        self.rope = _Rope()
+
+
+class _DiffusionModel(nn.Module):
+    """Stub diffusion model with N blocks and pos/neg conditioning buffers."""
+    def __init__(self, n_blocks=3, zero_conditioning=False, conditioning_dtype=torch.float32):
+        super().__init__()
+        self.blocks = nn.ModuleList([_Block() for _ in range(n_blocks)])
+        pos = torch.zeros if zero_conditioning else torch.ones
+        self.register_buffer("positive_conditioning", pos((2, 4), dtype=conditioning_dtype))
+        self.register_buffer("negative_conditioning", torch.zeros((3, 4), dtype=conditioning_dtype))
+
+
+class _ModelInner:
+    """Inner model wrapper exposing `.diffusion_model`."""
+    def __init__(self, diffusion_model):
+        self.diffusion_model = diffusion_model
+
+
+class _ModelPatcher:
+    """ModelPatcher stub exposing `.model._ModelInner`."""
+    def __init__(self, diffusion_model):
+        self.model = _ModelInner(diffusion_model)
+
+
+def test_seedvr2_conditioning_schema_exposes_model_passthrough_output():
+    nodes_seedvr, restore = _import_nodes_seedvr_isolated()
+    try:
+        schema = nodes_seedvr.SeedVR2Conditioning.define_schema()
+        assert [input_item.id for input_item in schema.inputs] == [
+            "model",
+            "vae_conditioning",
+        ]
+        assert schema.inputs[1].display_name == "latent"
+        assert [output.display_name for output in schema.outputs] == [
+            "model",
+            "positive",
+            "negative",
+            "latent",
+        ]
+    finally:
+        restore()
+
+
+def test_seedvr2_conditioning_returns_packed_input_latent_deterministically():
+    nodes_seedvr, restore = _import_nodes_seedvr_isolated()
+    try:
+        diffusion_model = _DiffusionModel()
+        patcher = _ModelPatcher(diffusion_model)
+        samples = torch.arange(1, 25, dtype=torch.float32).reshape(1, 2, 3, 2, 2)
+        vae_conditioning = {"samples": samples}
+
+        _, first_positive, first_negative, first_latent = (
+            nodes_seedvr.SeedVR2Conditioning.execute(
+                patcher,
+                vae_conditioning,
+            )
+        )
+        _, second_positive, second_negative, second_latent = (
+            nodes_seedvr.SeedVR2Conditioning.execute(
+                patcher,
+                vae_conditioning,
+            )
+        )
+
+        expected_latent = samples.reshape(1, 6, 2, 2)
+        channel_last = samples.movedim(1, -1).contiguous()
+        expected_condition = torch.cat(
+            [
+                channel_last,
+                torch.ones((*channel_last.shape[:-1], 1)),
+            ],
+            dim=-1,
+        ).movedim(-1, 1).reshape(1, 9, 2, 2)
+
+        assert torch.equal(first_latent["samples"], expected_latent)
+        assert torch.equal(second_latent["samples"], expected_latent)
+        assert torch.equal(
+            first_positive[0][1]["condition"],
+            expected_condition,
+        )
+        assert torch.equal(
+            second_positive[0][1]["condition"],
+            expected_condition,
+        )
+        assert torch.equal(
+            first_negative[0][1]["condition"],
+            expected_condition,
+        )
+        assert torch.equal(
+            second_negative[0][1]["condition"],
+            expected_condition,
+        )
+    finally:
+        restore()
+
+
+def test_seedvr2_conditioning_fails_loud_on_zero_buffers():
+    nodes_seedvr, restore = _import_nodes_seedvr_isolated()
+    try:
+        diffusion_model = _DiffusionModel(zero_conditioning=True)
+        patcher = _ModelPatcher(diffusion_model)
+        vae_conditioning = {"samples": torch.zeros((1, 2, 1, 1, 1))}
+
+        with pytest.raises(RuntimeError) as excinfo:
+            nodes_seedvr.SeedVR2Conditioning.execute(
+                patcher, vae_conditioning,
+            )
+
+        message = str(excinfo.value)
+        assert message.startswith(
+            nodes_seedvr._SEEDVR2_INVALID_MODEL_MSG_PREFIX
+        ), (
+            "Fail-loud message must use the standard "
+            "_SEEDVR2_INVALID_MODEL_MSG_PREFIX so callers/log scrapers "
+            f"can match it. Got: {message!r}"
+        )
+        assert "positive_conditioning" in message
+        assert "negative_conditioning" in message
+    finally:
+        restore()

tests-unit/comfy_extras_test/test_seedvr2_nodes.py

@@ -0,0 +1,55 @@
+import importlib
+import inspect
+import sys
+from unittest.mock import MagicMock, patch
+
+import torch
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+
+def test_seedvr_node_signature_matches_schema():
+    mock_mm = MagicMock()
+    mock_mm.xformers_enabled.return_value = False
+    mock_mm.xformers_enabled_vae.return_value = False
+    mock_mm.sage_attention_enabled.return_value = False
+    mock_mm.flash_attention_enabled.return_value = False
+
+    sentinel = object()
+    prior_cpu = cli_args.cpu
+    cli_args.cpu = True
+    prior_module = sys.modules.get("comfy_extras.nodes_seedvr", sentinel)
+    comfy_pkg = sys.modules.get("comfy")
+    prior_mm_attr = getattr(comfy_pkg, "model_management", sentinel) if comfy_pkg else sentinel
+
+    with patch.dict(sys.modules, {"comfy.model_management": mock_mm}):
+        if comfy_pkg is not None:
+            setattr(comfy_pkg, "model_management", mock_mm)
+        sys.modules.pop("comfy_extras.nodes_seedvr", None)
+        try:
+            nodes_seedvr = importlib.import_module("comfy_extras.nodes_seedvr")
+            for node_cls in (nodes_seedvr.SeedVR2Preprocess, nodes_seedvr.SeedVR2PostProcessing, nodes_seedvr.SeedVR2Conditioning, nodes_seedvr.SeedVR2ProgressiveSampler):
+                schema_ids = [i.id for i in node_cls.define_schema().inputs]
+                exec_params = [
+                    p for p in inspect.signature(node_cls.execute).parameters.keys()
+                    if p != "cls"
+                ]
+                assert schema_ids == exec_params, (
+                    f"{node_cls.__name__} schema/execute drift: "
+                    f"schema_ids={schema_ids}, exec_params={exec_params}"
+                )
+        finally:
+            cli_args.cpu = prior_cpu
+            if prior_module is sentinel:
+                sys.modules.pop("comfy_extras.nodes_seedvr", None)
+            else:
+                sys.modules["comfy_extras.nodes_seedvr"] = prior_module
+            if comfy_pkg is not None:
+                if prior_mm_attr is sentinel:
+                    if hasattr(comfy_pkg, "model_management"):
+                        delattr(comfy_pkg, "model_management")
+                else:
+                    setattr(comfy_pkg, "model_management", prior_mm_attr)

tests-unit/comfy_extras_test/test_seedvr2_post_processing.py

@@ -0,0 +1,57 @@
+from unittest.mock import patch
+
+import torch
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+from comfy_extras import nodes_seedvr  # noqa: E402
+
+
+def _schema_ids(items):
+    return [item.id for item in items]
+
+
+def test_seedvr2_post_processing_schema():
+    schema = nodes_seedvr.SeedVR2PostProcessing.define_schema()
+
+    assert _schema_ids(schema.inputs) == ["images", "original_resized_images", "color_correction_method"]
+    assert schema.inputs[2].options == ["lab", "wavelet", "adain", "none"]
+    assert schema.inputs[2].default == "lab"
+    assert schema.outputs[0].get_io_type() == "IMAGE"
+
+
+def test_seedvr2_post_processing_oom_error_uses_color_correction_method(monkeypatch):
+    decoded = torch.full((1, 3, 4, 4), 0.25)
+    reference = torch.full((1, 3, 4, 4), 0.75)
+
+    def _lab(content, style):
+        raise torch.cuda.OutOfMemoryError("CUDA out of memory")
+
+    monkeypatch.setattr(nodes_seedvr.comfy.model_management, "vae_device", lambda: torch.device("cpu"))
+    monkeypatch.setattr(nodes_seedvr.comfy.model_management, "get_free_memory", lambda device: 1_000_000)
+    monkeypatch.setattr(nodes_seedvr.comfy.model_management, "soft_empty_cache", lambda: None)
+
+    with patch.object(nodes_seedvr, "lab_color_transfer", _lab):
+        try:
+            nodes_seedvr.SeedVR2PostProcessing._color_transfer_chunked(
+                decoded, reference, torch.device("cpu"), "lab",
+            )
+        except RuntimeError as exc:
+            assert "color_correction_method=lab" in str(exc)
+            assert " method=lab" not in str(exc)
+        else:
+            raise AssertionError("expected RuntimeError for one-frame LAB OOM")
+
+
+def test_seedvr2_post_processing_unknown_color_correction_method_raises():
+    decoded = torch.zeros(1, 2, 4, 4, 3)
+    original = torch.zeros(1, 2, 4, 4, 3)
+    try:
+        nodes_seedvr.SeedVR2PostProcessing.execute(decoded, original, "bogus")
+    except ValueError as exc:
+        assert "color_correction_method" in str(exc)
+    else:
+        raise AssertionError("expected ValueError for unknown color_correction_method")

tests-unit/comfy_test/model_detection_test.py

@@ -73,6 +73,24 @@ def _make_flux_schnell_comfyui_sd():
     return sd
 
 
+def _make_seedvr2_7b_separate_mm_sd():
+    return {
+        "blocks.35.mlp.vid.proj_in.weight": torch.empty(1, 3072),
+    }
+
+
+def _make_seedvr2_7b_shared_mm_sd():
+    return {
+        "blocks.35.mlp.all.proj_in_gate.weight": torch.empty(1, 1),
+    }
+
+
+def _make_seedvr2_3b_shared_mm_sd():
+    return {
+        "blocks.31.mlp.all.proj_in_gate.weight": torch.empty(1, 1),
+    }
+
+
 class TestModelDetection:
     """Verify that first-match model detection selects the correct model
     based on list ordering and unet_config specificity."""
@@ -125,6 +143,45 @@ class TestModelDetection:
         assert model_config is not None
         assert type(model_config).__name__ == "FluxSchnell"
 
+    def test_seedvr2_7b_separate_mm_detection_config(self):
+        sd = _make_seedvr2_7b_separate_mm_sd()
+        unet_config = detect_unet_config(sd, "")
+
+        assert unet_config is not None
+        assert unet_config["image_model"] == "seedvr2"
+        assert unet_config["vid_dim"] == 3072
+        assert unet_config["heads"] == 24
+        assert unet_config["num_layers"] == 36
+        assert unet_config["mm_layers"] == 36
+        assert unet_config["mlp_type"] == "normal"
+        assert unet_config["rope_type"] == "rope3d"
+        assert unet_config["rope_dim"] == 64
+
+    def test_seedvr2_7b_shared_mm_detection_config(self):
+        sd = _make_seedvr2_7b_shared_mm_sd()
+        unet_config = detect_unet_config(sd, "")
+
+        assert unet_config is not None
+        assert unet_config["image_model"] == "seedvr2"
+        assert unet_config["vid_dim"] == 3072
+        assert unet_config["heads"] == 24
+        assert unet_config["num_layers"] == 36
+        assert unet_config["mm_layers"] == 10
+        assert unet_config["mlp_type"] == "swiglu"
+        assert unet_config["rope_type"] == "rope3d"
+        assert unet_config["rope_dim"] == 64
+
+    def test_seedvr2_3b_shared_mm_detection_config(self):
+        sd = _make_seedvr2_3b_shared_mm_sd()
+        unet_config = detect_unet_config(sd, "")
+
+        assert unet_config is not None
+        assert unet_config["image_model"] == "seedvr2"
+        assert unet_config["vid_dim"] == 2560
+        assert unet_config["heads"] == 20
+        assert unet_config["num_layers"] == 32
+        assert unet_config["mlp_type"] == "swiglu"
+
     def test_unet_config_and_required_keys_combination_is_unique(self):
         """Each model in the registry must have a unique combination of
         ``unet_config`` and ``required_keys``. If two models share the same

tests-unit/comfy_test/seedvr_vae_forward_test.py

@@ -0,0 +1,86 @@
+"""Regression: ``comfy.ldm.seedvr.vae.VideoAutoencoderKL.forward`` must
+honor the actual tensor/tuple return contract of ``encode()`` and
+``decode_()`` and must NOT dereference diffusers-style ``.latent_dist``
+or ``.sample`` attributes on those returns.
+
+The pre-fix body raised ``AttributeError: 'Tensor' object has no
+attribute 'latent_dist'`` for ``mode in {"encode", "all"}`` and
+``AttributeError: 'VideoAutoencoderKL' object has no attribute 'decode'``
+for ``mode == "decode"`` (the class only defines ``decode_`` with a
+trailing underscore). The post-fix body unwraps the optional one-element
+tuple shape that ``return_dict=False`` produces and returns the tensor
+directly.
+
+Tests construct a stub subclass of ``VideoAutoencoderKL`` that bypasses
+the heavy ``__init__`` via ``torch.nn.Module.__init__(self)`` and
+overrides ``encode``/``decode_`` with known tensors so the contract can
+be probed without loading any real VAE weights.
+"""
+
+import torch
+import torch.nn as nn
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+from comfy.ldm.seedvr.vae import VideoAutoencoderKL  # noqa: E402
+
+
+_LATENT_SHAPE = (1, 16, 2, 2, 2)
+_DECODED_SHAPE = (1, 3, 5, 16, 16)
+_INPUT_ENCODE_SHAPE = (1, 3, 5, 16, 16)
+_INPUT_DECODE_SHAPE = (1, 16, 2, 2, 2)
+
+
+class _StubVAE(VideoAutoencoderKL):
+    def __init__(self):
+        nn.Module.__init__(self)
+        self._encode_out = torch.zeros(*_LATENT_SHAPE)
+        self._decode_out = torch.zeros(*_DECODED_SHAPE)
+
+    def encode(self, x, return_dict=True):
+        return self._encode_out
+
+    def decode_(self, z, return_dict=True):
+        return self._decode_out
+
+
+def test_forward_encode_returns_tensor():
+    vae = _StubVAE()
+    x = torch.zeros(*_INPUT_ENCODE_SHAPE)
+    result = vae.forward(x, mode="encode")
+    assert type(result) is torch.Tensor
+    assert result.shape == torch.Size(_LATENT_SHAPE)
+
+
+def test_forward_decode_returns_tensor():
+    vae = _StubVAE()
+    z = torch.zeros(*_INPUT_DECODE_SHAPE)
+    result = vae.forward(z, mode="decode")
+    assert type(result) is torch.Tensor
+    assert result.shape == torch.Size(_DECODED_SHAPE)
+
+
+class _TupleReturningStubVAE(VideoAutoencoderKL):
+    """Stub whose ``encode``/``decode_`` return the ``(tensor,)`` tuple of ``return_dict=False``, exercising the unwrap branch of ``VideoAutoencoderKL.forward``."""
+
+    def __init__(self):
+        nn.Module.__init__(self)
+        self._encode_tensor = torch.zeros(*_LATENT_SHAPE)
+        self._decode_tensor = torch.zeros(*_DECODED_SHAPE)
+
+    def encode(self, x, return_dict=True):
+        return (self._encode_tensor,)
+
+    def decode_(self, z, return_dict=True):
+        return (self._decode_tensor,)
+
+
+def test_forward_all_unwraps_one_tuple_at_each_step():
+    vae = _TupleReturningStubVAE()
+    x = torch.zeros(*_INPUT_ENCODE_SHAPE)
+    result = vae.forward(x, mode="all")
+    assert type(result) is torch.Tensor
+    assert result.shape == torch.Size(_DECODED_SHAPE)

tests-unit/comfy_test/test_seedvr2_dtype.py

@@ -0,0 +1,49 @@
+import torch
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+import comfy.sd
+import comfy.supported_models
+import comfy.ldm.seedvr.model as seedvr_model
+import comfy.ldm.seedvr.vae as seedvr_vae
+
+
+def test_seedvr2_fp16_manual_cast_only_for_bf16_device(monkeypatch):
+    bf16_device = object()
+    fp16_device = object()
+
+    monkeypatch.setattr(
+        comfy.supported_models.comfy.model_management,
+        "should_use_bf16",
+        lambda device=None: device is bf16_device,
+    )
+
+    bf16_config = comfy.supported_models.SeedVR2({"image_model": "seedvr2"})
+    bf16_config.set_inference_dtype(torch.float16, None, device=bf16_device)
+    assert bf16_config.manual_cast_dtype is torch.bfloat16
+
+    fp16_config = comfy.supported_models.SeedVR2({"image_model": "seedvr2"})
+    fp16_config.set_inference_dtype(torch.float16, None, device=fp16_device)
+    assert fp16_config.manual_cast_dtype is None
+
+
+def test_seedvr2_text_conditioning_accepts_cfg1_single_branch():
+    context = torch.arange(6, dtype=torch.float32).reshape(1, 3, 2)
+
+    txt, txt_shape = seedvr_model.NaDiT._resolve_text_conditioning(object(), context, [0])
+
+    torch.testing.assert_close(txt, context.squeeze(0))
+    torch.testing.assert_close(txt_shape, torch.tensor([[3]], device=context.device))
+
+
+def test_seedvr2_vae_decode_memory_covers_full_frame_lab_transfer():
+    wrapper = seedvr_vae.VideoAutoencoderKLWrapper.__new__(seedvr_vae.VideoAutoencoderKLWrapper)
+    estimate = wrapper.comfy_memory_used_decode((1, 16, 26, 120, 160))
+    old_estimate = 16 * 120 * 160 * (4 * 8 * 8) * 2
+
+    assert estimate == 101 * 960 * 1280 * 160
+    assert estimate > 15 * 1024 ** 3
+    assert estimate > old_estimate * 100

tests-unit/comfy_test/test_seedvr2_internals.py

@@ -0,0 +1,216 @@
+"""Consolidated SeedVR2 internals regression tests.
+
+Sources (all merged verbatim, helper names disambiguated where colliding):
+
+  * GroupNorm limit gate — causal_norm_wrapper at vae.py:509 must compare
+    memory_occupy against get_norm_limit(), not float('inf').
+  * SeedVR2 variable-length attention split-loop contract.
+
+Pre-import CPU-only guard is required because comfy.ldm.seedvr.model and
+comfy.ldm.modules.attention transitively pull in comfy.model_management,
+which probes torch.cuda.current_device() at import time unless args.cpu is
+set first.
+"""
+
+from __future__ import annotations
+
+from unittest.mock import patch
+
+import pytest
+import torch
+
+from comfy.cli_args import args
+
+if not torch.cuda.is_available():
+    args.cpu = True
+
+import comfy.ldm.seedvr.model as seedvr_model  # noqa: E402
+import comfy.ldm.seedvr.vae as vae_mod  # noqa: E402
+import comfy.ldm.modules.attention as attention  # noqa: E402
+import comfy.ops as comfy_ops  # noqa: E402
+from comfy.ldm.seedvr.vae import (  # noqa: E402
+    causal_norm_wrapper,
+    set_norm_limit,
+)
+from comfy.ldm.seedvr.attention import var_attention_optimized_split  # noqa: E402
+
+
+# ---------------------------------------------------------------------------
+# GroupNorm limit tests (test_seedvr_groupnorm_limit.py)
+# ---------------------------------------------------------------------------
+
+_NUM_CHANNELS = 8
+_NUM_GROUPS = 4
+_TENSOR_SHAPE = (1, 8, 2, 4, 4)
+
+_GROUPNORM_SUBCLASSES = [
+    pytest.param(comfy_ops.disable_weight_init.GroupNorm, id="disable_weight_init"),
+    pytest.param(comfy_ops.manual_cast.GroupNorm, id="manual_cast"),
+]
+
+
+@pytest.mark.parametrize("groupnorm_cls", _GROUPNORM_SUBCLASSES)
+def test_seedvr_groupnorm_low_limit_uses_chunked_groupnorm_path(groupnorm_cls):
+    real_group_norm = vae_mod.F.group_norm
+    set_norm_limit(1e-9)
+    try:
+        gn = groupnorm_cls(num_channels=_NUM_CHANNELS, num_groups=_NUM_GROUPS)
+        gn.eval()
+
+        forward_hook_calls = []
+
+        def _hook(module, inputs, output):
+            forward_hook_calls.append(tuple(inputs[0].shape))
+
+        spy_calls = []
+
+        def _group_norm_spy(input_tensor, num_groups_arg, *args, **kwargs):
+            spy_calls.append({"num_groups": int(num_groups_arg)})
+            return real_group_norm(input_tensor, num_groups_arg, *args, **kwargs)
+
+        handle = gn.register_forward_hook(_hook)
+        try:
+            with patch.object(vae_mod.F, "group_norm", side_effect=_group_norm_spy):
+                out_tensor = causal_norm_wrapper(gn, torch.randn(*_TENSOR_SHAPE))
+        finally:
+            handle.remove()
+
+        full_calls = len(forward_hook_calls)
+        chunked_calls = sum(1 for entry in spy_calls if entry["num_groups"] < _NUM_GROUPS)
+
+        assert tuple(int(s) for s in out_tensor.shape) == _TENSOR_SHAPE
+        assert full_calls == 0, (
+            f"low-limit GroupNorm gate must NOT take the full-forward path; got full_calls={full_calls}"
+        )
+        assert chunked_calls > 0, (
+            f"low-limit GroupNorm gate must take the chunked path; got chunked_calls={chunked_calls}"
+        )
+    finally:
+        set_norm_limit(None)
+
+
+# ---------------------------------------------------------------------------
+# SeedVR2 var_attention split-loop tests
+# ---------------------------------------------------------------------------
+
+def test_seedvr2_7b_swin_attention_forward_uses_optimized_var_attention(monkeypatch):
+    dim = 8
+    heads = 2
+    head_dim = 4
+    attn = seedvr_model.NaSwinAttention(
+        vid_dim=dim,
+        txt_dim=dim,
+        heads=heads,
+        head_dim=head_dim,
+        qk_bias=False,
+        qk_norm=seedvr_model.CustomRMSNorm,
+        qk_norm_eps=1e-6,
+        rope_type=None,
+        rope_dim=head_dim,
+        shared_weights=False,
+        window=(2, 1, 1),
+        window_method="720pwin_by_size_bysize",
+        version=True,
+        device="cpu",
+        dtype=torch.float32,
+        operations=comfy_ops.disable_weight_init,
+    )
+    generator = torch.Generator(device="cpu").manual_seed(11)
+    vid = torch.randn(8, dim, generator=generator)
+    txt = torch.randn(3, dim, generator=generator)
+    vid_shape = torch.tensor([[2, 2, 2]], dtype=torch.long)
+    txt_shape = torch.tensor([[3]], dtype=torch.long)
+    calls = []
+
+    def fake_optimized_var_attention(**kwargs):
+        calls.append(kwargs)
+        return kwargs["q"]
+
+    monkeypatch.setattr(seedvr_model, "optimized_var_attention", fake_optimized_var_attention)
+
+    vid_out, txt_out = attn(vid, txt, vid_shape, txt_shape, seedvr_model.Cache(disable=True))
+
+    assert tuple(vid_out.shape) == (8, dim)
+    assert tuple(txt_out.shape) == (3, dim)
+    assert len(calls) == 1
+    call = calls[0]
+    assert tuple(call["q"].shape) == (14, heads, head_dim)
+    assert tuple(call["k"].shape) == (14, heads, head_dim)
+    assert tuple(call["v"].shape) == (14, heads, head_dim)
+    assert call["heads"] == heads
+    assert call["skip_reshape"] is True
+    assert call["skip_output_reshape"] is True
+    torch.testing.assert_close(
+        call["cu_seqlens_q"],
+        torch.tensor([0, 7, 14], dtype=torch.int32),
+        rtol=0,
+        atol=0,
+    )
+    torch.testing.assert_close(
+        call["cu_seqlens_k"],
+        torch.tensor([0, 7, 14], dtype=torch.int32),
+        rtol=0,
+        atol=0,
+    )
+
+
+def test_var_attention_optimized_split_calls_dense_backend_per_window(monkeypatch):
+    heads = 2
+    head_dim = 3
+    q = torch.arange(30, dtype=torch.float32).reshape(5, heads, head_dim)
+    k = q + 100
+    v = q + 200
+    cu = torch.tensor([0, 2, 5], dtype=torch.int32)
+    calls = []
+
+    def fake_optimized_attention(q_arg, k_arg, v_arg, heads_arg, **kwargs):
+        calls.append(
+            {
+                "q_shape": tuple(q_arg.shape),
+                "k_shape": tuple(k_arg.shape),
+                "v_shape": tuple(v_arg.shape),
+                "heads": heads_arg,
+                "kwargs": kwargs,
+            }
+        )
+        return q_arg + v_arg
+
+    monkeypatch.setattr(attention, "optimized_attention", fake_optimized_attention)
+
+    out = var_attention_optimized_split(
+        q,
+        k,
+        v,
+        heads,
+        cu,
+        cu,
+        skip_reshape=True,
+        skip_output_reshape=True,
+    )
+
+    assert tuple(out.shape) == (5, heads, head_dim)
+    assert len(calls) == 2
+    assert calls[0]["q_shape"] == (1, heads, 2, head_dim)
+    assert calls[1]["q_shape"] == (1, heads, 3, head_dim)
+    assert all(call["heads"] == heads for call in calls)
+    assert all(call["kwargs"]["skip_reshape"] is True for call in calls)
+    assert all(call["kwargs"]["skip_output_reshape"] is True for call in calls)
+    torch.testing.assert_close(out, q + v, rtol=0, atol=0)
+
+
+def test_var_attention_optimized_split_rejects_bad_offsets():
+    q = torch.randn(5, 2, 3)
+    cu_bad = torch.tensor([0, 2, 6], dtype=torch.int32)
+    cu_ok = torch.tensor([0, 2, 5], dtype=torch.int32)
+
+    with pytest.raises(ValueError, match="cu_seqlens_q does not match token count"):
+        var_attention_optimized_split(
+            q,
+            q,
+            q,
+            2,
+            cu_bad,
+            cu_ok,
+            skip_reshape=True,
+            skip_output_reshape=True,
+        )

tests-unit/comfy_test/test_seedvr2_model.py

@@ -0,0 +1,307 @@
+"""Consolidated SeedVR2 model/graph/forward regression tests.
+
+Merged from:
+- seedvr_model_test.py
+- test_seedvr_7b_final_block_text_path.py
+- test_seedvr_forward_no_device_cast.py
+- test_seedvr_latent_format.py
+- test_seedvr2_vae_graph_boundaries.py
+"""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock
+
+import torch
+from torch import nn
+
+from comfy.cli_args import args
+
+if not torch.cuda.is_available():
+    args.cpu = True
+
+import comfy  # noqa: E402
+import comfy.latent_formats  # noqa: E402
+import comfy.ldm.seedvr.model  # noqa: E402
+import comfy.ldm.seedvr.model as seedvr_model  # noqa: E402
+import comfy.ldm.seedvr.vae as seedvr_vae_mod  # noqa: E402
+import comfy.model_management  # noqa: E402
+import comfy.sample  # noqa: E402
+import comfy.sd as sd_mod  # noqa: E402
+import nodes as nodes_mod  # noqa: E402
+from comfy.ldm.seedvr.model import NaDiT  # noqa: E402
+
+
+# ---------------------------------------------------------------------------
+# Helpers from seedvr_model_test.py
+# ---------------------------------------------------------------------------
+
+
+def _make_standin(positive_conditioning):
+    class _StandIn(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.register_buffer(
+                "positive_conditioning", positive_conditioning
+            )
+
+        _resolve_text_conditioning = NaDiT._resolve_text_conditioning
+
+    return _StandIn()
+
+
+# ---------------------------------------------------------------------------
+# Helpers from test_seedvr_7b_final_block_text_path.py
+# ---------------------------------------------------------------------------
+
+
+class _StubModule(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+
+
+def _capture_last_layer_flags(monkeypatch, vid_dim: int, txt_in_dim: int) -> list[bool]:
+    flags = []
+
+    class _Block(_StubModule):
+        def __init__(self, *args, **kwargs):
+            flags.append(kwargs["is_last_layer"])
+            super().__init__()
+
+    monkeypatch.setattr(seedvr_model, "NaPatchIn", _StubModule)
+    monkeypatch.setattr(seedvr_model, "NaPatchOut", _StubModule)
+    monkeypatch.setattr(seedvr_model, "TimeEmbedding", _StubModule)
+    monkeypatch.setattr(seedvr_model, "NaMMSRTransformerBlock", _Block)
+
+    seedvr_model.NaDiT(
+        norm_eps=1e-5,
+        num_layers=4,
+        mlp_type="normal",
+        vid_dim=vid_dim,
+        txt_in_dim=txt_in_dim,
+        heads=24,
+        mm_layers=3,
+    )
+
+    return flags
+
+
+# ---------------------------------------------------------------------------
+# Helpers from test_seedvr_latent_format.py
+# ---------------------------------------------------------------------------
+
+
+class _Model:
+    def __init__(self, latent_format):
+        self._latent_format = latent_format
+
+    def get_model_object(self, name):
+        assert name == "latent_format"
+        return self._latent_format
+
+
+# ---------------------------------------------------------------------------
+# Helpers from test_seedvr2_vae_graph_boundaries.py
+# ---------------------------------------------------------------------------
+
+
+class _Patcher:
+    def get_free_memory(self, device):
+        return 1024 * 1024 * 1024
+
+
+class _EncodeWrapper(seedvr_vae_mod.VideoAutoencoderKLWrapper):
+    def __init__(self, encoded):
+        nn.Module.__init__(self)
+        self.encoded = encoded
+        self.spatial_downsample_factor = 8
+        self.temporal_downsample_factor = 4
+        self.seen = []
+
+    def encode(self, x):
+        self.seen.append(tuple(x.shape))
+        return self.encoded.to(device=x.device, dtype=x.dtype)
+
+
+class _DecodeWrapper(seedvr_vae_mod.VideoAutoencoderKLWrapper):
+    def __init__(self):
+        nn.Module.__init__(self)
+        self.spatial_downsample_factor = 8
+        self.temporal_downsample_factor = 4
+        self.calls = []
+
+    def decode(self, z, seedvr2_tiling=None):
+        self.calls.append({"shape": tuple(z.shape), "seedvr2_tiling": seedvr2_tiling})
+        if z.ndim == 4:
+            b, tc, h, w = z.shape
+            t = tc // 16
+        else:
+            b, _, t, h, w = z.shape
+        return torch.zeros(b, 3, t, h * 8, w * 8, dtype=z.dtype, device=z.device)
+
+
+def _make_vae(wrapper):
+    vae = sd_mod.VAE.__new__(sd_mod.VAE)
+    vae.first_stage_model = wrapper
+    vae.device = torch.device("cpu")
+    vae.output_device = torch.device("cpu")
+    vae.vae_dtype = torch.float32
+    vae.latent_channels = 16
+    vae.latent_dim = 3
+    vae.downscale_ratio = (lambda a: max(0, (a + 3) // 4), 8, 8)
+    vae.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
+    vae.output_channels = 3
+    vae.disable_offload = True
+    vae.extra_1d_channel = None
+    vae.crop_input = False
+    vae.not_video = False
+    vae.patcher = _Patcher()
+    vae.process_input = lambda image: image
+    vae.process_output = lambda image: image.add(1.0).div(2.0).clamp(0.0, 1.0)
+    vae.vae_output_dtype = lambda: torch.float32
+    vae.memory_used_encode = lambda shape, dtype: 1
+    vae.memory_used_decode = lambda shape, dtype: 1
+    vae.throw_exception_if_invalid = lambda: None
+    vae.vae_encode_crop_pixels = lambda pixels: pixels
+    vae.spacial_compression_decode = lambda: 8
+    vae.temporal_compression_decode = lambda: 4
+    return vae
+
+
+# ---------------------------------------------------------------------------
+# Tests from seedvr_model_test.py
+# ---------------------------------------------------------------------------
+
+
+def test_missing_context_falls_back_to_positive_buffer():
+    """``context is None`` falls back to the registered ``positive_conditioning`` buffer and runs to completion."""
+    pos_buffer = torch.full((58, 5120), 7.0)
+    standin = _make_standin(pos_buffer)
+    txt, txt_shape = standin._resolve_text_conditioning(None)
+    assert txt.shape == (58, 5120)
+    assert (txt == 7.0).all(), (
+        "fallback path must use the positive_conditioning buffer "
+        "verbatim, not a zero tensor"
+    )
+    assert txt_shape.shape == (1, 1)
+    assert txt_shape[0, 0].item() == 58
+
+
+# ---------------------------------------------------------------------------
+# Tests from test_seedvr_7b_final_block_text_path.py
+# ---------------------------------------------------------------------------
+
+
+def test_seedvr2_7b_keeps_final_block_text_path(monkeypatch):
+    assert _capture_last_layer_flags(monkeypatch, vid_dim=3072, txt_in_dim=3072) == [
+        False,
+        False,
+        False,
+        False,
+    ]
+
+
+def test_seedvr2_7b_rope3d_matches_wrapper_oracle():
+    rope = seedvr_model.get_na_rope("rope3d", dim=64)
+    generator = torch.Generator(device="cpu").manual_seed(0)
+    q = torch.randn(4, 2, 128, generator=generator)
+    k = torch.randn(4, 2, 128, generator=generator)
+    shape = torch.tensor([[1, 2, 2]], dtype=torch.long)
+    freqs = rope.get_axial_freqs(1, 2, 2).reshape(4, -1)
+
+    expected_q = seedvr_model._apply_seedvr2_rotary_emb(
+        freqs,
+        q.permute(1, 0, 2).float(),
+    ).to(q.dtype).permute(1, 0, 2)
+    expected_k = seedvr_model._apply_seedvr2_rotary_emb(
+        freqs,
+        k.permute(1, 0, 2).float(),
+    ).to(k.dtype).permute(1, 0, 2)
+
+    actual_q, actual_k = rope(q.clone(), k.clone(), shape, seedvr_model.Cache(disable=True))
+
+    torch.testing.assert_close(actual_q, expected_q, rtol=0, atol=0)
+    torch.testing.assert_close(actual_k, expected_k, rtol=0, atol=0)
+
+
+# ---------------------------------------------------------------------------
+# Tests from test_seedvr_latent_format.py
+# ---------------------------------------------------------------------------
+
+
+def test_seedvr2_latent_format_uses_16_channels_without_3d_empty_latent_expansion():
+    latent_format = comfy.latent_formats.SeedVR2()
+    latent_image = torch.zeros(1, 1, 4, 5)
+
+    fixed = comfy.sample.fix_empty_latent_channels(_Model(latent_format), latent_image)
+
+    assert latent_format.latent_channels == 16
+    assert latent_format.latent_dimensions == 2
+    assert fixed.shape == (1, 16, 4, 5)
+
+
+# ---------------------------------------------------------------------------
+# Tests from test_seedvr2_vae_graph_boundaries.py
+# ---------------------------------------------------------------------------
+
+
+def test_seedvr2_encode_and_encode_tiled_preserve_native_latent_contract(monkeypatch):
+    monkeypatch.setattr(sd_mod.model_management, "load_models_gpu", lambda *a, **k: None)
+
+    encoded = torch.full((1, 16, 2, 4, 5), 2.0)
+    vae = _make_vae(_EncodeWrapper(encoded))
+    pixels = torch.zeros(1, 5, 32, 40, 3)
+
+    node_output = nodes_mod.VAEEncode().encode(vae, pixels)[0]
+    node_latent = node_output["samples"]
+    assert set(node_output) == {"samples"}
+    assert tuple(node_latent.shape) == (1, 16, 2, 4, 5)
+    assert node_latent.dtype == torch.float32
+    assert node_latent.stride()[-1] == 1
+    assert torch.equal(node_latent, torch.full_like(node_latent, 2.0 * 0.9152))
+
+    tiled = torch.full((1, 16, 2, 4, 5), 3.0)
+    monkeypatch.setattr(seedvr_vae_mod, "tiled_vae", MagicMock(return_value=tiled))
+    tiled_output = nodes_mod.VAEEncodeTiled().encode(
+        vae,
+        pixels,
+        tile_size=512,
+        overlap=64,
+        temporal_size=16,
+        temporal_overlap=4,
+    )[0]
+    tiled_latent = tiled_output["samples"]
+    assert set(tiled_output) == {"samples"}
+    assert tuple(tiled_latent.shape) == (1, 16, 2, 4, 5)
+    assert tiled_latent.dtype == torch.float32
+    assert torch.equal(tiled_latent, torch.full_like(tiled_latent, 3.0 * 0.9152))
+
+
+def test_vaedecode_tiled_spatial_applies_temporal_discarded(monkeypatch):
+    monkeypatch.setattr(sd_mod.model_management, "load_models_gpu", lambda *a, **k: None)
+    vae = _make_vae(_DecodeWrapper())
+
+    nodes_mod.VAEDecodeTiled().decode(
+        vae,
+        {"samples": torch.zeros(1, 16, 2, 4, 5)},
+        tile_size=512,
+        overlap=64,
+        temporal_size=16,
+        temporal_overlap=4,
+    )
+
+    # Spatial inputs flow through; temporal inputs are discarded — SeedVR2 owns
+    # temporal via the MemoryState causal cache, so VAEDecodeTiled's temporal
+    # knobs are no-ops at the wrapper.
+    assert vae.first_stage_model.calls == [
+        {
+            "shape": (1, 16, 2, 4, 5),
+            "seedvr2_tiling": {
+                "enable_tiling": True,
+                "tile_size": (512, 512),
+                "tile_overlap": (64, 64),
+                "temporal_size": 0,
+                "temporal_overlap": 0,
+            },
+        }
+    ]

tests-unit/comfy_test/test_seedvr2_vae_decode.py

@@ -0,0 +1,91 @@
+from unittest.mock import patch
+
+import pytest
+import torch
+import torch.nn as nn
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+import comfy.ldm.seedvr.vae as vae_mod  # noqa: E402
+from comfy_extras import nodes_seedvr  # noqa: E402
+
+
+def _make_wrapper() -> vae_mod.VideoAutoencoderKLWrapper:
+    wrapper = vae_mod.VideoAutoencoderKLWrapper.__new__(
+        vae_mod.VideoAutoencoderKLWrapper
+    )
+    nn.Module.__init__(wrapper)
+    return wrapper
+
+
+def _fingerprint_decode_(self, z, return_dict=True):
+    b = int(z.shape[0])
+    t = int(z.shape[2])
+    h = int(z.shape[3])
+    w = int(z.shape[4])
+    out = torch.empty(b, 3, t, h * 8, w * 8)
+    for batch_idx in range(b):
+        out[batch_idx].fill_(float(batch_idx + 1))
+    return out
+
+
+def _decode_with_patches(wrapper, z):
+    with patch.object(vae_mod.VideoAutoencoderKL, "decode_", _fingerprint_decode_):
+        return wrapper.decode(z)
+
+
+def test_decode_b2_t3_multi_frame_batch_unchanged():
+    wrapper = _make_wrapper()
+
+    out = _decode_with_patches(wrapper, torch.zeros(2, 16 * 3, 2, 2))
+
+    assert tuple(out.shape) == (2, 3, 3, 16, 16)
+
+
+class _Wrapper(vae_mod.VideoAutoencoderKLWrapper):
+    def __init__(self):
+        nn.Module.__init__(self)
+        self.calls = []
+
+    def parameters(self):
+        return iter([torch.nn.Parameter(torch.zeros(()))])
+
+def _decode_stub(self, latent):
+    self.calls.append(tuple(latent.shape))
+    return torch.zeros(latent.shape[0], 3, latent.shape[2], latent.shape[3] * 8, latent.shape[4] * 8)
+
+
+def test_seedvr2_wrapper_decode_accepts_5d_channel_first_latents_without_preprocessor_state():
+    wrapper = _Wrapper()
+
+    with patch.object(vae_mod.VideoAutoencoderKL, "decode_", _decode_stub):
+        out = wrapper.decode(torch.zeros(1, 16, 2, 4, 5))
+
+    assert tuple(out.shape) == (1, 3, 2, 32, 40)
+    assert wrapper.calls == [(1, 16, 2, 4, 5)]
+
+
+def test_seedvr2_wrapper_decode_rejects_wrong_rank_latents():
+    wrapper = _Wrapper()
+
+    with pytest.raises(RuntimeError, match=r"latent input must be 4-D collapsed .* or 5-D"):
+        wrapper.decode(torch.zeros(1, 16, 4))
+
+
+def _t_padded(t_in: int) -> int:
+    if t_in == 1:
+        return 1
+    if t_in <= 4:
+        return 5
+    if (t_in - 1) % 4 == 0:
+        return t_in
+    return t_in + (4 - ((t_in - 1) % 4))
+
+
+@pytest.mark.parametrize("t_in", [1, 5, 9])
+def test_t_padded_matches_cut_videos(t_in):
+    dummy = torch.zeros(1, t_in, 1, 1, 1)
+    assert nodes_seedvr.cut_videos(dummy).shape[1] == _t_padded(t_in)

tests-unit/comfy_test/test_seedvr2_vae_tiled.py

@@ -0,0 +1,348 @@
+from contextlib import ExitStack
+from unittest.mock import MagicMock, patch
+
+import torch
+import torch.nn as nn
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+import comfy.ldm.seedvr.vae as vae_mod  # noqa: E402
+import comfy.ldm.seedvr.vae as seedvr_vae_mod  # noqa: E402
+import comfy.sd as sd_mod  # noqa: E402
+from comfy.ldm.seedvr.vae import MemoryState, tiled_vae  # noqa: E402
+
+
+# ---------------------------------------------------------------------------
+# From test_seedvr_vae_tiled_decode_latent_min_size_override.py
+# ---------------------------------------------------------------------------
+
+
+def test_runtime_decode_zero_temporal_size_disables_slicing_for_call():
+    from comfy.ldm.seedvr.vae import MemoryState, VideoAutoencoderKL, tiled_vae
+
+    class StubVAEModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.slicing_latent_min_size = 2
+            self.spatial_downsample_factor = 8
+            self.temporal_downsample_factor = 4
+            self.device = torch.device("cpu")
+            self.use_slicing = True
+            self._dummy = torch.nn.Parameter(torch.zeros(1, dtype=torch.float32))
+            self.decode_min_sizes = []
+            self.memory_states = []
+
+        def decode_(self, t_chunk):
+            self.decode_min_sizes.append(self.slicing_latent_min_size)
+            return VideoAutoencoderKL.slicing_decode(self, t_chunk)
+
+        def _decode(self, z, memory_state=MemoryState.DISABLED):
+            self.memory_states.append(memory_state)
+            b, c, d, h, w = z.shape
+            return torch.zeros((b, 3, d, h * 8, w * 8), dtype=z.dtype)
+
+    vae = StubVAEModel()
+    z = torch.zeros((1, 16, 5, 8, 8), dtype=torch.float32)
+
+    tiled_vae(
+        z,
+        vae,
+        tile_size=(64, 64),
+        tile_overlap=(0, 0),
+        temporal_size=0,
+        temporal_overlap=0,
+        encode=False,
+    )
+
+    assert vae.decode_min_sizes == [5]
+    assert vae.memory_states == [MemoryState.DISABLED]
+    assert vae.slicing_latent_min_size == 2
+
+
+# ---------------------------------------------------------------------------
+# From test_seedvr_vae_tiled_encode_runt_slice_override.py
+# ---------------------------------------------------------------------------
+
+
+def test_zero_temporal_size_preserves_min_size_when_encode_raises():
+    from comfy.ldm.seedvr.vae import tiled_vae
+
+    class RaisingVAEModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.slicing_sample_min_size = 4
+            self.spatial_downsample_factor = 8
+            self.temporal_downsample_factor = 4
+            self.device = torch.device("cpu")
+            self._dummy = torch.nn.Parameter(torch.zeros(1, dtype=torch.float32))
+
+        def encode(self, t_chunk):
+            raise RuntimeError("simulated encode failure")
+
+    vae = RaisingVAEModel()
+    x = torch.zeros((1, 3, 12, 64, 64), dtype=torch.float32)
+
+    raised = False
+    try:
+        tiled_vae(
+            x,
+            vae,
+            tile_size=(64, 64),
+            tile_overlap=(0, 0),
+            temporal_size=0,
+            temporal_overlap=0,
+            encode=True,
+        )
+    except RuntimeError as exc:
+        if "simulated encode failure" not in str(exc):
+            raise
+        raised = True
+
+    assert raised
+    assert vae.slicing_sample_min_size == 4
+
+
+# ---------------------------------------------------------------------------
+# From test_seedvr_vae_tiled_temporal_slicing.py
+# ---------------------------------------------------------------------------
+
+
+class _SlicingDecodeVAE(nn.Module):
+    def __init__(self, slicing_latent_min_size):
+        super().__init__()
+        self.slicing_latent_min_size = slicing_latent_min_size
+        self.spatial_downsample_factor = 8
+        self.temporal_downsample_factor = 4
+        self.device = torch.device("cpu")
+        self.use_slicing = True
+        self._dummy = nn.Parameter(torch.zeros(1, dtype=torch.float32))
+        self.decode_min_sizes = []
+        self.memory_states = []
+
+    def decode_(self, z):
+        self.decode_min_sizes.append(self.slicing_latent_min_size)
+        return vae_mod.VideoAutoencoderKL.slicing_decode(self, z)
+
+    def _decode(self, z, memory_state=MemoryState.DISABLED):
+        self.memory_states.append(memory_state)
+        x = z[:, :1].repeat(
+            1,
+            3,
+            1,
+            self.spatial_downsample_factor,
+            self.spatial_downsample_factor,
+        )
+        return x
+
+
+def test_decode_tiled_vae_maps_temporal_args_to_latent_slicing_min_size():
+    vae = _SlicingDecodeVAE(slicing_latent_min_size=2)
+    z = torch.arange(1 * 16 * 5 * 8 * 8, dtype=torch.float32).reshape(1, 16, 5, 8, 8)
+
+    tiled_vae(
+        z,
+        vae,
+        tile_size=(64, 64),
+        tile_overlap=(0, 0),
+        temporal_size=12,
+        temporal_overlap=4,
+        encode=False,
+    )
+
+    assert vae.decode_min_sizes == [2]
+    assert vae.memory_states == [MemoryState.INITIALIZING, MemoryState.ACTIVE]
+    assert vae.slicing_latent_min_size == 2
+
+    wrapper = vae_mod.VideoAutoencoderKLWrapper.__new__(
+        vae_mod.VideoAutoencoderKLWrapper
+    )
+    nn.Module.__init__(wrapper)
+    seedvr2_tiling = {
+        "enable_tiling": True,
+        "tile_size": (64, 64),
+        "tile_overlap": (0, 0),
+        "temporal_size": 8,
+        "temporal_overlap": 7,
+    }
+
+    captured = {}
+
+    def _fake_tiled_vae(latent, model, **kwargs):
+        captured.update(kwargs)
+        return torch.zeros(1, 3, 1, 16, 16)
+
+    with patch.object(vae_mod, "tiled_vae", side_effect=_fake_tiled_vae):
+        wrapper.decode(torch.zeros(1, 16, 2, 2), seedvr2_tiling=seedvr2_tiling)
+
+    assert captured["temporal_overlap"] == 7
+
+
+# ---------------------------------------------------------------------------
+# From test_vae_decode_tiled_dispatcher_seedvr2_4d.py
+# ---------------------------------------------------------------------------
+
+
+def _force_oom(*a, **k):
+    raise torch.cuda.OutOfMemoryError("forced OOM for dispatcher test")
+
+
+def _make_vae(first_stage_model, latent_channels, latent_dim):
+    vae = sd_mod.VAE.__new__(sd_mod.VAE)
+    vae.first_stage_model = first_stage_model
+    vae.patcher = MagicMock()
+    vae.patcher.get_free_memory = MagicMock(return_value=8 * 1024 * 1024 * 1024)
+    vae.device = vae.output_device = torch.device("cpu")
+    vae.vae_dtype = torch.float32
+    vae.disable_offload = True
+    vae.extra_1d_channel = None
+    vae.upscale_ratio = vae.downscale_ratio = 8
+    vae.upscale_index_formula = vae.downscale_index_formula = None
+    vae.output_channels = 3
+    vae.latent_channels = latent_channels
+    vae.latent_dim = latent_dim
+    vae.vae_output_dtype = lambda: torch.float32
+    vae.spacial_compression_decode = lambda: 8
+    vae.process_input = lambda x: x
+    vae.process_output = lambda x: x
+    vae.throw_exception_if_invalid = lambda: None
+    vae.memory_used_decode = lambda *a, **k: 1
+    return vae
+
+
+def _dispatch(vae, samples, seedvr2_call, generic_call, patch_wrapper_decode):
+    mm = sd_mod.model_management
+    with ExitStack() as stack:
+        stack.enter_context(patch.object(mm, "raise_non_oom", lambda e: None))
+        stack.enter_context(patch.object(mm, "load_models_gpu", lambda *a, **k: None))
+        stack.enter_context(patch.object(mm, "soft_empty_cache", lambda: None))
+        stack.enter_context(patch.object(sd_mod.VAE, "_decode_tiled_owned", seedvr2_call))
+        stack.enter_context(patch.object(sd_mod.VAE, "decode_tiled_", generic_call))
+        if patch_wrapper_decode:
+            stack.enter_context(patch.object(
+                seedvr_vae_mod.VideoAutoencoderKLWrapper, "decode",
+                side_effect=_force_oom))
+        vae.decode(samples)
+
+
+def test_4d_seedvr2_latent_routes_to_owned_decode_tiled():
+    wrapper = seedvr_vae_mod.VideoAutoencoderKLWrapper.__new__(
+        seedvr_vae_mod.VideoAutoencoderKLWrapper)
+    vae = _make_vae(wrapper, latent_channels=16, latent_dim=3)
+    seedvr2_call = MagicMock(return_value=torch.zeros(1, 3, 9, 64, 64))
+    generic_call = MagicMock(return_value=torch.zeros(1, 3, 64, 64))
+    _dispatch(vae, torch.zeros(1, 16 * 3, 8, 8), seedvr2_call, generic_call, True)
+    assert seedvr2_call.call_count == 1
+    assert generic_call.call_count == 0
+
+
+def test_4d_non_seedvr2_latent_still_routes_to_generic_decode_tiled():
+    first_stage = MagicMock()
+    first_stage.comfy_handles_tiling = False
+    first_stage.decode = MagicMock(side_effect=_force_oom)
+    vae = _make_vae(first_stage, latent_channels=4, latent_dim=2)
+    seedvr2_call = MagicMock(return_value=torch.zeros(1, 3, 9, 64, 64))
+    generic_call = MagicMock(return_value=torch.zeros(1, 3, 64, 64))
+    _dispatch(vae, torch.zeros(1, 4, 8, 8), seedvr2_call, generic_call, False)
+    assert generic_call.call_count == 1
+    assert seedvr2_call.call_count == 0
+
+
+# ---------------------------------------------------------------------------
+# From test_vae_encode_tiled_fallback_dispatcher_seedvr2.py
+# ---------------------------------------------------------------------------
+
+
+def _populate_common_vae_attrs_fallback(vae):
+    vae.patcher = MagicMock()
+    vae.patcher.get_free_memory = MagicMock(return_value=8 * 1024 * 1024 * 1024)
+    vae.device = torch.device("cpu")
+    vae.output_device = torch.device("cpu")
+    vae.vae_dtype = torch.float32
+    vae.disable_offload = True
+    vae.extra_1d_channel = None
+    vae.upscale_ratio = 8
+    vae.upscale_index_formula = None
+    vae.output_channels = 3
+    vae.latent_channels = 16
+    vae.latent_dim = 3
+    vae.downscale_ratio = 8
+    vae.downscale_index_formula = None
+    vae.not_video = False
+    vae.crop_input = False
+    vae.pad_channel_value = None
+
+    vae.vae_output_dtype = lambda: torch.float32
+    vae.spacial_compression_encode = lambda: 8
+    vae.process_input = lambda x: x
+    vae.process_output = lambda x: x
+    vae.throw_exception_if_invalid = lambda: None
+    vae.memory_used_encode = lambda *a, **k: 1
+
+
+def _make_seedvr2_vae_fallback():
+    vae = sd_mod.VAE.__new__(sd_mod.VAE)
+    wrapper = seedvr_vae_mod.VideoAutoencoderKLWrapper.__new__(
+        seedvr_vae_mod.VideoAutoencoderKLWrapper
+    )
+    vae.first_stage_model = wrapper
+    _populate_common_vae_attrs_fallback(vae)
+    return vae
+
+
+def _make_non_seedvr2_vae_fallback():
+    vae = sd_mod.VAE.__new__(sd_mod.VAE)
+    vae.first_stage_model = MagicMock()
+    vae.first_stage_model.comfy_handles_tiling = False
+    _populate_common_vae_attrs_fallback(vae)
+    return vae
+
+
+def _force_regular_encode_oom(*args, **kwargs):
+    raise torch.cuda.OutOfMemoryError("forced OOM for dispatcher test")
+
+
+def test_seedvr2_3d_routes_to_owned_encode_tiled_on_oom():
+    vae = _make_seedvr2_vae_fallback()
+    pixel_samples = torch.zeros((1, 8, 64, 64, 3))
+
+    seedvr2_call = MagicMock(return_value=torch.zeros(1, 16, 2, 8, 8))
+    generic_call = MagicMock(return_value=torch.zeros(1, 16, 2, 8, 8))
+
+    with patch.object(sd_mod.model_management, "raise_non_oom",
+                      lambda e: None), \
+         patch.object(sd_mod.model_management, "load_models_gpu",
+                      lambda *a, **k: None), \
+         patch.object(sd_mod.model_management, "soft_empty_cache",
+                      lambda: None), \
+         patch.object(seedvr_vae_mod.VideoAutoencoderKLWrapper, "encode",
+                      side_effect=_force_regular_encode_oom), \
+         patch.object(sd_mod.VAE, "_encode_tiled_owned", seedvr2_call), \
+         patch.object(sd_mod.VAE, "encode_tiled_3d", generic_call):
+        vae.encode(pixel_samples)
+
+    assert seedvr2_call.call_count == 1, (
+        f"Expected _encode_tiled_owned to be called once for a SeedVR2 3D "
+        f"input under OOM fallback; got {seedvr2_call.call_count} calls."
+    )
+    assert generic_call.call_count == 0, (
+        f"encode_tiled_3d must NOT be called for a SeedVR2 input; got "
+        f"{generic_call.call_count} calls."
+    )
+
+
+def test_non_seedvr2_encode_tiled_3d_default_overlap_is_concrete():
+    vae = _make_non_seedvr2_vae_fallback()
+    vae.downscale_ratio = (lambda a: max(1, a // 4), 8, 8)
+    vae.upscale_ratio = (lambda a: a * 4, 8, 8)
+    generic_call = MagicMock(return_value=torch.zeros(1, 16, 2, 8, 8))
+    pixel_samples = torch.zeros((1, 8, 64, 64, 3))
+
+    with patch.object(sd_mod.model_management, "load_models_gpu",
+                      lambda *a, **k: None), \
+         patch.object(sd_mod.VAE, "encode_tiled_3d", generic_call):
+        vae.encode_tiled(pixel_samples)
+
+    assert generic_call.call_args.kwargs["overlap"] == (1, 64, 64)

tests-unit/comfy_test/test_seedvr_progressive_sampler.py

@@ -0,0 +1,95 @@
+"""Unit tests for ``comfy_extras.nodes_seedvr.SeedVR2ProgressiveSampler``."""
+
+from unittest.mock import patch
+
+import pytest
+import torch
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+import comfy.sample  # noqa: E402
+import comfy_extras.nodes_seedvr as nodes_seedvr_mod  # noqa: E402
+from comfy_extras.nodes_seedvr import SeedVR2ProgressiveSampler  # noqa: E402
+
+_LAT_C = 16
+_COND_C = 17
+
+
+def _make_inputs(B: int = 1, T: int = 5, H: int = 8, W: int = 8):
+    """Build minimal SeedVR2-shaped sampling inputs."""
+    samples_5d = torch.arange(
+        B * _LAT_C * T * H * W, dtype=torch.float32
+    ).reshape(B, _LAT_C, T, H, W)
+    samples = samples_5d.reshape(B, _LAT_C * T, H, W).contiguous()
+
+    cond_5d = torch.arange(
+        B * _COND_C * T * H * W, dtype=torch.float32
+    ).reshape(B, _COND_C, T, H, W) + 10000.0
+    cond = cond_5d.reshape(B, _COND_C * T, H, W).contiguous()
+
+    text_pos = torch.zeros(1, 4, 32)
+    text_neg = torch.zeros(1, 4, 32)
+    positive = [[text_pos, {"condition": cond.clone()}]]
+    negative = [[text_neg, {"condition": cond.clone()}]]
+    latent_image = {"samples": samples}
+    return latent_image, positive, negative, samples_5d, cond_5d
+
+
+def _identity_fix_empty(model, latent_image, downscale_ratio_spacial=None):
+    return latent_image
+
+
+def _fingerprinted_prepare_noise(latent_image, seed, batch_inds=None):
+    """Return a tensor whose values encode ``(seed, position)``."""
+    base = torch.arange(
+        latent_image.numel(), dtype=torch.float32
+    ).reshape(latent_image.shape)
+    return base + float(seed) * 1e6
+
+
+def test_progressive_sampler_schema_exposes_manual_default_auto_chunking():
+    schema = SeedVR2ProgressiveSampler.define_schema()
+    inputs = {item.id: item for item in schema.inputs}
+
+    assert inputs["chunking_mode"].options == ["manual", "auto"]
+    assert inputs["chunking_mode"].default == "manual"
+
+
+def test_vram_seed_frames_per_chunk_predicts_4n1_clamped_to_t_pixel():
+    """VRAM chunk-size law: seed = nearest 4n+1 to 4*(free_GB - 3), clamped to [1, t_pixel]."""
+    gib = 1024 ** 3
+    seed = nodes_seedvr_mod._seedvr2_vram_seed_frames_per_chunk
+    assert seed(20 * gib, 65) == 65   # 4*(20-3)=68 -> 4n+1 69 -> clamp to t_pixel 65
+    assert seed(6 * gib, 97) == 13    # 4*(6-3)=12 -> nearest 4n+1 13
+    assert seed(2 * gib, 97) == 1     # below margin -> floor at 1
+
+
+@pytest.mark.parametrize("bad_chunk", [0, -1, 2])
+def test_t3_invalid_frames_per_chunk_raises_value_error(bad_chunk):
+    """``frames_per_chunk`` violating 4n+1 (or <1) must raise ``ValueError`` before any model invocation."""
+    latent, pos, neg, _, _ = _make_inputs(T=5)
+
+    sampler_called = {"n": 0}
+
+    def _should_not_be_called(*args, **kwargs):
+        sampler_called["n"] += 1
+        return torch.zeros(1)
+
+    with patch.object(comfy.sample, "sample",
+                      side_effect=_should_not_be_called), \
+         patch.object(comfy.sample, "fix_empty_latent_channels",
+                      side_effect=_identity_fix_empty), \
+         patch.object(comfy.sample, "prepare_noise",
+                      side_effect=_fingerprinted_prepare_noise):
+        with pytest.raises(ValueError) as excinfo:
+            SeedVR2ProgressiveSampler.execute(
+                model=None, seed=0, steps=2, cfg=1.0,
+                sampler_name="euler", scheduler="simple",
+                positive=pos, negative=neg, latent=latent,
+                denoise=1.0, frames_per_chunk=bad_chunk, temporal_overlap=0,
+            )
+    assert str(bad_chunk) in str(excinfo.value)
+    assert sampler_called["n"] == 0