diff --git a/comfy/ldm/lens/model.py b/comfy/ldm/lens/model.py
new file mode 100644
index 000000000..7bff7f6af
--- /dev/null
+++ b/comfy/ldm/lens/model.py
@@ -0,0 +1,513 @@
+"""Lens denoising transformer (DiT)"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.ldm.flux.layers
+import comfy.patcher_extension
+from comfy.ldm.flux.layers import EmbedND
+from comfy.ldm.flux.math import apply_rope
+from comfy.ldm.modules.attention import optimized_attention
+
+
+def _lens_time_proj(t: torch.Tensor, dim: int = 256) -> torch.Tensor:
+    return comfy.ldm.flux.layers.timestep_embedding(t, dim)
+
+
+def _lens_position_ids(
+    frame: int, height: int, width: int, text_seq_len: int,
+    scale_rope: bool = True, device=None,
+) -> torch.Tensor:
+    """Lens axial (frame, h, w) position ids for joint image + text sequence.
+
+    With ``scale_rope=True`` h/w are centered around 0 (negative + positive
+    halves) and text starts at ``max(h//2, w//2)``. Result shape ``[seq, 3]``;
+    caller adds a batch dim for ``EmbedND``.
+    """
+    if scale_rope:
+        h_pos = torch.cat([torch.arange(-(height - height // 2), 0, device=device),
+                           torch.arange(0, height // 2, device=device)])
+        w_pos = torch.cat([torch.arange(-(width - width // 2), 0, device=device),
+                           torch.arange(0, width // 2, device=device)])
+        text_start = max(height // 2, width // 2)
+    else:
+        h_pos = torch.arange(height, device=device)
+        w_pos = torch.arange(width, device=device)
+        text_start = max(height, width)
+
+    f_pos = torch.arange(frame, device=device)
+    img_ids = torch.zeros(frame, height, width, 3, device=device)
+    img_ids[..., 0] = f_pos[:, None, None]
+    img_ids[..., 1] = h_pos[None, :, None]
+    img_ids[..., 2] = w_pos[None, None, :]
+    img_ids = img_ids.reshape(-1, 3)
+
+    # Text positions replicate across all 3 axes (matches original packing).
+    txt_pos = torch.arange(text_start, text_start + text_seq_len, device=device).float()
+    txt_ids = txt_pos[:, None].expand(text_seq_len, 3)
+
+    return torch.cat([img_ids, txt_ids], dim=0)
+
+
+class _TimestepEmbedder(nn.Module):
+    def __init__(self, in_channels: int, time_embed_dim: int, dtype=None, device=None, operations=None) -> None:
+        super().__init__()
+        self.linear_1 = operations.Linear(in_channels, time_embed_dim, dtype=dtype, device=device)
+        self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.linear_1(x)
+        x = F.silu(x)
+        return self.linear_2(x)
+
+
+class LensTimestepProjEmbeddings(nn.Module):
+    def __init__(self, embedding_dim: int, dtype=None, device=None, operations=None) -> None:
+        super().__init__()
+        self.timestep_embedder = _TimestepEmbedder(256, embedding_dim, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, timestep: torch.Tensor, hidden_states: torch.Tensor) -> torch.Tensor:
+        proj = _lens_time_proj(timestep, 256)
+        return self.timestep_embedder(proj.to(dtype=hidden_states.dtype))
+
+
+class GateMLP(nn.Module):
+    """SwiGLU MLP."""
+
+    def __init__(self, dim: int, hidden_dim: int, dtype=None, device=None, operations=None) -> None:
+        super().__init__()
+        self.w1 = operations.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device)
+        self.w2 = operations.Linear(hidden_dim, dim, bias=False, dtype=dtype, device=device)
+        self.w3 = operations.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device)
+
+    def forward(self, x):
+        return self.w2(F.silu(self.w1(x), inplace=True).mul_(self.w3(x)))
+
+
+class LensJointAttention(nn.Module):
+    """Joint image+text attention with fused QKV per stream."""
+
+    def __init__(
+        self,
+        query_dim: int,
+        added_kv_proj_dim: int,
+        dim_head: int = 64,
+        heads: int = 8,
+        out_dim: Optional[int] = None,
+        eps: float = 1e-5,
+        dtype=None,
+        device=None,
+        operations=None,
+    ) -> None:
+        super().__init__()
+        self.inner_dim = out_dim if out_dim is not None else dim_head * heads
+        self.heads = self.inner_dim // dim_head
+        self.dim_head = dim_head
+        self.out_dim = out_dim if out_dim is not None else query_dim
+
+        self.norm_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)
+        self.norm_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)
+        self.norm_added_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)
+        self.norm_added_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)
+
+        self.img_qkv = operations.Linear(query_dim, 3 * self.inner_dim, bias=True, dtype=dtype, device=device)
+        self.txt_qkv = operations.Linear(added_kv_proj_dim, 3 * self.inner_dim, bias=True, dtype=dtype, device=device)
+
+        # ModuleList([Linear, Identity]) for state-dict key compatibility.
+        self.to_out = nn.ModuleList([
+            operations.Linear(self.inner_dim, self.out_dim, bias=True, dtype=dtype, device=device),
+            nn.Identity(),
+        ])
+        self.to_add_out = operations.Linear(self.inner_dim, query_dim, bias=True, dtype=dtype, device=device)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        transformer_options: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        bsz, seq_img, _ = hidden_states.shape
+        seq_txt = encoder_hidden_states.shape[1]
+
+        # image stream
+        img_qkv = self.img_qkv(hidden_states).view(bsz, seq_img, 3, self.heads, self.dim_head)
+        img_q, img_k, img_v = img_qkv.unbind(dim=2)
+        img_q = self.norm_q(img_q)
+        img_k = self.norm_k(img_k)
+        img_v = img_v.contiguous()
+        del img_qkv
+
+        # text stream
+        txt_qkv = self.txt_qkv(encoder_hidden_states).view(bsz, seq_txt, 3, self.heads, self.dim_head)
+        txt_q, txt_k, txt_v = txt_qkv.unbind(dim=2)
+        txt_q = self.norm_added_q(txt_q)
+        txt_k = self.norm_added_k(txt_k)
+        txt_v = txt_v.contiguous()
+        del txt_qkv
+
+        # [B, S, H, D] → [B, H, S, D] for attention, dels to avoid VRAM peaks
+        q = torch.cat([img_q, txt_q], dim=1).transpose(1, 2)
+        del img_q, txt_q
+        k = torch.cat([img_k, txt_k], dim=1).transpose(1, 2)
+        del img_k, txt_k
+        v = torch.cat([img_v, txt_v], dim=1).transpose(1, 2)
+        del img_v, txt_v
+
+        q, k = apply_rope(q, k, freqs_cis)
+
+        if attention_mask is not None:
+            expected = (bsz, 1, 1, seq_img + seq_txt)
+            if attention_mask.shape != expected:
+                raise ValueError(
+                    f"attention_mask must be {expected}, got {tuple(attention_mask.shape)}"
+                )
+            attention_mask = attention_mask.to(q.dtype)
+
+        out = optimized_attention(
+            q, k, v, self.heads, mask=attention_mask, skip_reshape=True,
+            transformer_options=transformer_options,
+        )
+
+        img_out = self.to_out[1](self.to_out[0](out[:, :seq_img, :]))
+        txt_out = self.to_add_out(out[:, seq_img:, :])
+        return img_out, txt_out
+
+
+class LensTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        eps: float = 1e-6,
+        rms_norm: bool = True,
+        dtype=None,
+        device=None,
+        operations=None,
+    ) -> None:
+        super().__init__()
+
+        self.attn = LensJointAttention(
+            query_dim=dim,
+            added_kv_proj_dim=dim,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            out_dim=dim,
+            eps=1e-5,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        if rms_norm:
+            NormCls = operations.RMSNorm
+            norm_kwargs = {}
+        else:
+            NormCls = operations.LayerNorm
+            norm_kwargs = {"elementwise_affine": False}
+
+        mlp_hidden = int(dim / 3 * 8)
+
+        # Sequential(SiLU, Linear) so state-dict lands at img_mod.1.{weight,bias}.
+        self.img_mod = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device),
+        )
+        self.img_norm1 = NormCls(dim, eps=eps, dtype=dtype, device=device, **norm_kwargs)
+        self.img_norm2 = NormCls(dim, eps=eps, dtype=dtype, device=device, **norm_kwargs)
+        self.img_mlp = GateMLP(dim, mlp_hidden, dtype=dtype, device=device, operations=operations)
+
+        self.txt_mod = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device),
+        )
+        self.txt_norm1 = NormCls(dim, eps=eps, dtype=dtype, device=device, **norm_kwargs)
+        self.txt_norm2 = NormCls(dim, eps=eps, dtype=dtype, device=device, **norm_kwargs)
+        self.txt_mlp = GateMLP(dim, mlp_hidden, dtype=dtype, device=device, operations=operations)
+
+    @staticmethod
+    def _modulate(x: torch.Tensor, mod_params: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        shift, scale, gate = mod_params.chunk(3, dim=-1)
+        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1), gate.unsqueeze(1)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        transformer_options: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        img_mod1, img_mod2 = self.img_mod(temb).chunk(2, dim=-1)
+        txt_mod1, txt_mod2 = self.txt_mod(temb).chunk(2, dim=-1)
+
+        img_modulated, img_gate1 = self._modulate(self.img_norm1(hidden_states), img_mod1)
+        txt_modulated, txt_gate1 = self._modulate(self.txt_norm1(encoder_hidden_states), txt_mod1)
+
+        img_attn, txt_attn = self.attn(
+            hidden_states=img_modulated,
+            encoder_hidden_states=txt_modulated,
+            freqs_cis=freqs_cis,
+            attention_mask=attention_mask,
+            transformer_options=transformer_options,
+        )
+
+        hidden_states = hidden_states + img_gate1 * img_attn
+        encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn
+
+        img_modulated2, img_gate2 = self._modulate(self.img_norm2(hidden_states), img_mod2)
+        hidden_states = hidden_states + img_gate2 * self.img_mlp(img_modulated2)
+
+        txt_modulated2, txt_gate2 = self._modulate(self.txt_norm2(encoder_hidden_states), txt_mod2)
+        encoder_hidden_states = encoder_hidden_states + txt_gate2 * self.txt_mlp(txt_modulated2)
+
+        return encoder_hidden_states, hidden_states
+
+
+class _AdaLayerNormContinuousNoAffine(nn.Module):
+    """AdaLayerNormContinuous(elementwise_affine=False).
+
+    The reference uses ``scale, shift = chunk(2)`` (scale first) — opposite
+    to Flux's ``LastLayer``.
+    """
+
+    def __init__(self, embedding_dim: int, conditioning_embedding_dim: int, eps: float = 1e-6,
+                 dtype=None, device=None, operations=None) -> None:
+        super().__init__()
+        self.linear = operations.Linear(
+            conditioning_embedding_dim, embedding_dim * 2, bias=True, dtype=dtype, device=device
+        )
+        self.eps = eps
+        self.embedding_dim = embedding_dim
+
+    def forward(self, x: torch.Tensor, conditioning: torch.Tensor) -> torch.Tensor:
+        emb = self.linear(F.silu(conditioning))
+        scale, shift = torch.chunk(emb, 2, dim=-1)
+        x = F.layer_norm(x, (self.embedding_dim,), None, None, self.eps)
+        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+class LensTransformer2DModel(nn.Module):
+    """Lens dual-stream MMDiT (48 blocks, inner_dim=1536, multi-layer text)."""
+
+    def __init__(
+        self,
+        patch_size: int = 2,
+        in_channels: int = 128,
+        out_channels: Optional[int] = 32,
+        num_layers: int = 48,
+        attention_head_dim: int = 64,
+        num_attention_heads: int = 24,
+        enc_hidden_dim: int = 2880,
+        axes_dims_rope: Tuple[int, int, int] = (8, 28, 28),
+        rms_norm: bool = True,
+        multi_layer_encoder_feature: bool = True,
+        selected_layer_index: Tuple[int, ...] = (5, 11, 17, 23),
+        image_model=None,  # unused; accepted for detection-side configs.
+        dtype=None,
+        device=None,
+        operations=None,
+    ) -> None:
+        super().__init__()
+        self.patch_size = patch_size
+        self.in_channels = in_channels
+        self.out_channels = out_channels if out_channels is not None else in_channels
+        self.inner_dim = num_attention_heads * attention_head_dim
+        self.multi_layer_encoder_feature = multi_layer_encoder_feature
+        self.selected_layer_index = list(selected_layer_index)
+        self.dtype = dtype
+
+        self.pos_embed = EmbedND(dim=attention_head_dim, theta=10000, axes_dim=list(axes_dims_rope))
+        self.time_text_embed = LensTimestepProjEmbeddings(
+            embedding_dim=self.inner_dim, dtype=dtype, device=device, operations=operations
+        )
+
+        if self.multi_layer_encoder_feature:
+            self.txt_norm = nn.ModuleList(
+                [operations.RMSNorm(enc_hidden_dim, eps=1e-5, dtype=dtype, device=device)
+                 for _ in self.selected_layer_index]
+            )
+            self.txt_in = operations.Linear(
+                enc_hidden_dim * len(self.selected_layer_index),
+                self.inner_dim, bias=True, dtype=dtype, device=device,
+            )
+        else:
+            self.txt_norm = operations.RMSNorm(enc_hidden_dim, eps=1e-5, dtype=dtype, device=device)
+            self.txt_in = operations.Linear(enc_hidden_dim, self.inner_dim, bias=True, dtype=dtype, device=device)
+
+        self.img_in = operations.Linear(in_channels, self.inner_dim, bias=True, dtype=dtype, device=device)
+
+        self.transformer_blocks = nn.ModuleList([
+            LensTransformerBlock(
+                dim=self.inner_dim,
+                num_attention_heads=num_attention_heads,
+                attention_head_dim=attention_head_dim,
+                eps=1e-6,
+                rms_norm=rms_norm,
+                dtype=dtype, device=device, operations=operations,
+            )
+            for _ in range(num_layers)
+        ])
+
+        self.norm_out = _AdaLayerNormContinuousNoAffine(
+            self.inner_dim, self.inner_dim, eps=1e-6,
+            dtype=dtype, device=device, operations=operations,
+        )
+        self.proj_out = operations.Linear(
+            self.inner_dim, patch_size * patch_size * self.out_channels, bias=True,
+            dtype=dtype, device=device,
+        )
+
+    def forward(self, x: torch.Tensor, timestep: torch.Tensor, context: torch.Tensor, attention_mask: Optional[torch.Tensor] = None,
+                transformer_options: Optional[Dict[str, Any]] = None, **kwargs) -> torch.Tensor:
+        if transformer_options is None:
+            transformer_options = {}
+        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
+            self._forward, self,
+            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options),
+        ).execute(x, timestep, context, attention_mask, transformer_options, **kwargs)
+
+    def _forward(
+        self,
+        x: torch.Tensor,
+        timestep: torch.Tensor,
+        context: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        transformer_options: Optional[Dict[str, Any]] = None,
+        control: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """ComfyUI bridge: ``(x[B,128,h,w], t[B], context[B,S,L*H], mask[B,S])``."""
+        if transformer_options is None:
+            transformer_options = {}
+        transformer_options = transformer_options.copy()
+        patches = transformer_options.get("patches", {})
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+
+        B, C, h, w = x.shape
+        hidden_states = x.permute(0, 2, 3, 1).reshape(B, h * w, C)
+
+        if self.multi_layer_encoder_feature:
+            L = len(self.selected_layer_index)
+            enc_dim = context.shape[-1] // L
+            encoder_hidden_states = list(
+                context.reshape(B, -1, L, enc_dim).unbind(dim=2)
+            )
+            text_seq_len = encoder_hidden_states[0].shape[1]
+        else:
+            encoder_hidden_states = context
+            text_seq_len = context.shape[1]
+
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (B, text_seq_len), dtype=torch.bool, device=x.device
+            )
+
+        img_len = h * w
+        joint_mask = self._build_joint_attention_mask(attention_mask, img_len)
+
+        hidden_states = self.img_in(hidden_states)
+        timestep = timestep.to(hidden_states.dtype)
+
+        if self.multi_layer_encoder_feature:
+            normed = [self.txt_norm[i](encoder_hidden_states[i]) for i in range(L)]
+            encoder_hidden_states = torch.cat(normed, dim=-1)
+        else:
+            encoder_hidden_states = self.txt_norm(encoder_hidden_states)
+        encoder_hidden_states = self.txt_in(encoder_hidden_states)
+
+        if "post_input" in patches:
+            for p in patches["post_input"]:
+                out = p({
+                    "img": hidden_states,
+                    "txt": encoder_hidden_states,
+                    "transformer_options": transformer_options,
+                })
+                hidden_states = out["img"]
+                encoder_hidden_states = out["txt"]
+
+        temb = self.time_text_embed(timestep, hidden_states)
+        ids = _lens_position_ids(1, h, w, text_seq_len, device=hidden_states.device).unsqueeze(0)
+        freqs_cis = self.pos_embed(ids)
+
+        transformer_options["total_blocks"] = len(self.transformer_blocks)
+        transformer_options["block_type"] = "double"
+        for i, block in enumerate(self.transformer_blocks):
+            transformer_options["block_index"] = i
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["txt"], out["img"] = block(
+                        hidden_states=args["img"],
+                        encoder_hidden_states=args["txt"],
+                        temb=args["vec"],
+                        freqs_cis=args["pe"],
+                        attention_mask=args.get("attn_mask"),
+                        transformer_options=args.get("transformer_options"),
+                    )
+                    return out
+                out = blocks_replace[("double_block", i)](
+                    {
+                        "img": hidden_states,
+                        "txt": encoder_hidden_states,
+                        "vec": temb,
+                        "pe": freqs_cis,
+                        "attn_mask": joint_mask,
+                        "transformer_options": transformer_options,
+                    },
+                    {"original_block": block_wrap},
+                )
+                encoder_hidden_states = out["txt"]
+                hidden_states = out["img"]
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    freqs_cis=freqs_cis,
+                    attention_mask=joint_mask,
+                    transformer_options=transformer_options,
+                )
+
+            if "double_block" in patches:
+                for p in patches["double_block"]:
+                    out = p({
+                        "img": hidden_states,
+                        "txt": encoder_hidden_states,
+                        "x": x,
+                        "block_index": i,
+                        "transformer_options": transformer_options,
+                    })
+                    hidden_states = out["img"]
+                    encoder_hidden_states = out["txt"]
+
+            if control is not None:
+                control_i = control.get("input")
+                if control_i is not None and i < len(control_i):
+                    add = control_i[i]
+                    if add is not None:
+                        hidden_states[:, :add.shape[1]] += add
+
+        hidden_states = self.norm_out(hidden_states, temb)
+        out = self.proj_out(hidden_states)
+        return out.reshape(B, h, w, C).permute(0, 3, 1, 2).contiguous()
+
+    @staticmethod
+    def _build_joint_attention_mask(text_mask: torch.Tensor, img_len: int) -> torch.Tensor:
+        if text_mask.dtype != torch.bool:
+            text_mask = text_mask.bool()
+        bsz = text_mask.shape[0]
+        img_ones = torch.ones((bsz, img_len), dtype=torch.bool, device=text_mask.device)
+        joint = torch.cat([img_ones, text_mask], dim=1)
+        additive = torch.zeros_like(joint, dtype=torch.float32)
+        additive.masked_fill_(~joint, torch.finfo(torch.float32).min)
+        return additive[:, None, None, :]
diff --git a/comfy/model_base.py b/comfy/model_base.py
index d81f13c69..d4ab1499e 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -35,6 +35,7 @@ import comfy.ldm.hydit.models
 import comfy.ldm.audio.dit
 import comfy.ldm.audio.embedders
 import comfy.ldm.flux.model
+import comfy.ldm.lens.model
 import comfy.ldm.lightricks.model
 import comfy.ldm.hunyuan_video.model
 import comfy.ldm.cosmos.model
@@ -1058,6 +1059,27 @@ class Flux2(Flux):
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         return out
 
+
+class Lens(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
+        super().__init__(
+            model_config, model_type, device=device,
+            unet_model=comfy.ldm.lens.model.LensTransformer2DModel,
+        )
+
+    def encode_adm(self, **kwargs):
+        return None  # Lens has no pooled/ADM conditioning.
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+        return out
+
 class GenmoMochi(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.genmo.joint_model.asymm_models_joint.AsymmDiTJoint)
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 70b4df8b3..2b0b98cd8 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -755,6 +755,30 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["timestep_scale"] = 1000.0
         return dit_config
 
+    if '{}transformer_blocks.0.attn.norm_added_q.weight'.format(key_prefix) in state_dict_keys \
+            and '{}transformer_blocks.0.img_mlp.w1.weight'.format(key_prefix) in state_dict_keys:  # Lens
+        img_in_w = state_dict['{}img_in.weight'.format(key_prefix)]
+        proj_out_w = state_dict['{}proj_out.weight'.format(key_prefix)]
+        multi_layer = '{}txt_norm.0.weight'.format(key_prefix) in state_dict_keys
+        if multi_layer:
+            enc_hidden_dim = state_dict['{}txt_norm.0.weight'.format(key_prefix)].shape[0]
+            # Indices are TE-side; the DiT just consumes L layers in order.
+            selected_layer_index = tuple(range(count_blocks(state_dict_keys, '{}txt_norm.'.format(key_prefix) + '{}.')))
+        else:
+            enc_hidden_dim = state_dict['{}txt_norm.weight'.format(key_prefix)].shape[0]
+            selected_layer_index = (0,)
+
+        return {
+            "image_model": "lens",
+            "in_channels": img_in_w.shape[1],
+            "out_channels": proj_out_w.shape[0] // 4,  # patch_size ** 2 (=2² default)
+            "num_layers": count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.'),
+            "num_attention_heads": img_in_w.shape[0] // 64,  # // attention_head_dim default
+            "enc_hidden_dim": enc_hidden_dim,
+            "multi_layer_encoder_feature": multi_layer,
+            "selected_layer_index": selected_layer_index,
+        }
+
     if '{}txt_norm.weight'.format(key_prefix) in state_dict_keys:  # Qwen Image
         dit_config = {}
         dit_config["image_model"] = "qwen_image"
diff --git a/comfy/ops.py b/comfy/ops.py
index 9bcd6c900..56445be8d 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -18,6 +18,7 @@
 
 import torch
 import logging
+import contextlib
 import comfy.model_management
 from comfy.cli_args import args, PerformanceFeature
 import comfy.float
@@ -1047,6 +1048,144 @@ class QuantLinearFunc(torch.autograd.Function):
 
         return grad_input, grad_weight, grad_bias, None, None, None
 
+# Quantized-weight module helpers
+
+def _quantized_apply(module, fn, recurse=True):
+    """Re-wrap Parameters after fn so .to()/.cuda() propagate through QuantizedTensor weights."""
+    if recurse:
+        for child in module.children():
+            child._apply(fn)
+    for key, param in module._parameters.items():
+        if param is None:
+            continue
+        p = fn(param)
+        if (not torch.is_inference_mode_enabled()) and p.is_inference():
+            p = p.clone()
+        module.register_parameter(key, torch.nn.Parameter(p, requires_grad=False))
+    for key, buf in module._buffers.items():
+        if buf is not None:
+            module._buffers[key] = fn(buf)
+    return module
+
+
+def _load_quantized_module(module, super_load, state_dict, prefix, local_metadata, strict,
+                            missing_keys, unexpected_keys, error_msgs, load_extra_params=False):
+    """Shared _load_from_state_dict body for quantized-weight modules.
+
+    Pops weight (+ scales, +/- extras), populates module.weight as a Parameter
+    or Parameter-wrapped QuantizedTensor, then calls super_load and strips
+    consumed keys from missing_keys. Reads compute_dtype from factory_kwargs
+    and disabled formats from module._disabled_formats.
+    """
+    device = module.factory_kwargs["device"]
+    compute_dtype = module.factory_kwargs["dtype"]
+    disabled_formats = module._disabled_formats
+    layer_name = prefix.rstrip('.')
+
+    weight = state_dict.pop(f"{prefix}weight", None)
+    if weight is None:
+        logging.warning(f"Missing weight for layer {layer_name}")
+        module.weight = None
+        return
+    manually_loaded_keys = [f"{prefix}weight"]
+
+    def pop_scale(name, dtype=None):
+        key = f"{prefix}{name}"
+        v = state_dict.pop(key, None)
+        if v is not None:
+            v = v.to(device=device)
+            if dtype is not None:
+                v = v.view(dtype=dtype)
+            manually_loaded_keys.append(key)
+        return v
+
+    layer_conf = state_dict.pop(f"{prefix}comfy_quant", None)
+    if layer_conf is not None:
+        layer_conf = json.loads(layer_conf.numpy().tobytes())
+
+    if layer_conf is None:
+        module.weight = torch.nn.Parameter(weight.to(device=device, dtype=compute_dtype), requires_grad=False)
+    else:
+        module.quant_format = layer_conf.get("format", None)
+        module._full_precision_mm_config = layer_conf.get("full_precision_matrix_mult", False)
+        if not module._full_precision_mm:
+            module._full_precision_mm = module._full_precision_mm_config
+        if module.quant_format in disabled_formats:
+            module._full_precision_mm = True
+        if module.quant_format is None:
+            raise ValueError(f"Unknown quantization format for layer {layer_name}")
+
+        qconfig = QUANT_ALGOS[module.quant_format]
+        module.layout_type = qconfig["comfy_tensor_layout"]
+        layout_cls = get_layout_class(module.layout_type)
+
+        # Per-format scales; fp8 dtype views handle both legacy uint8-on-disk and native fp8.
+        if module.quant_format in ("float8_e4m3fn", "float8_e5m2"):
+            scales = {"scale": pop_scale("weight_scale")}
+        elif module.quant_format == "mxfp8":
+            bs = pop_scale("weight_scale", torch.float8_e8m0fnu)
+            if bs is None:
+                raise ValueError(f"Missing MXFP8 block scales for layer {layer_name}")
+            scales = {"scale": bs}
+        elif module.quant_format == "nvfp4":
+            ts = pop_scale("weight_scale_2")
+            bs = pop_scale("weight_scale", torch.float8_e4m3fn)
+            if ts is None or bs is None:
+                raise ValueError(f"Missing NVFP4 scales for layer {layer_name}")
+            scales = {"scale": ts, "block_scale": bs}
+        else:
+            raise ValueError(f"Unsupported quantization format: {module.quant_format}")
+
+        params = layout_cls.Params(**scales, orig_dtype=compute_dtype, orig_shape=module._orig_shape)
+        module.weight = torch.nn.Parameter(
+            QuantizedTensor(weight.to(device=device, dtype=qconfig["storage_t"]), module.layout_type, params),
+            requires_grad=False,
+        )
+
+        if load_extra_params:
+            for param_name in qconfig["parameters"]:
+                if param_name in {"weight_scale", "weight_scale_2"}:
+                    continue
+                param_key = f"{prefix}{param_name}"
+                _v = state_dict.pop(param_key, None)
+                if _v is None:
+                    continue
+                module.register_parameter(param_name, torch.nn.Parameter(_v.to(device=device), requires_grad=False))
+                manually_loaded_keys.append(param_key)
+
+    super_load(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
+    for key in manually_loaded_keys:
+        if key in missing_keys:
+            missing_keys.remove(key)
+
+
+def _quantized_weight_state_dict(module, sd, prefix, extra_quant_conf=None, extra_quant_params=()):
+    """Shared state_dict body. extra_quant_conf merges into the comfy_quant JSON;
+    extra_quant_params names attributes written as additional top-level keys."""
+    if not hasattr(module, 'weight'):
+        logging.warning(f"Warning: state dict on uninitialized op {prefix}")
+        return sd
+    bias = getattr(module, 'bias', None)
+    if bias is not None:
+        sd[f"{prefix}bias"] = bias
+    if module.weight is None:
+        return sd
+    if isinstance(module.weight, QuantizedTensor):
+        sd.update(module.weight.state_dict(f"{prefix}weight"))
+        quant_conf = {"format": module.quant_format}
+        if getattr(module, '_full_precision_mm_config', False):
+            quant_conf["full_precision_matrix_mult"] = True
+        if extra_quant_conf:
+            quant_conf.update(extra_quant_conf)
+        sd[f"{prefix}comfy_quant"] = torch.tensor(list(json.dumps(quant_conf).encode("utf-8")), dtype=torch.uint8)
+        for name in extra_quant_params:
+            value = getattr(module, name, None)
+            if value is not None:
+                sd[f"{prefix}{name}"] = value
+    else:
+        sd[f"{prefix}weight"] = module.weight
+    return sd
+
 
 def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False, disabled=[]):
     class MixedPrecisionOps(manual_cast):
@@ -1056,21 +1195,16 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
         _disabled = disabled
 
         class Linear(torch.nn.Module, CastWeightBiasOp):
-            def __init__(
-                self,
-                in_features: int,
-                out_features: int,
-                bias: bool = True,
-                device=None,
-                dtype=None,
-            ) -> None:
+            _disabled_formats = disabled
+
+            def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None, dtype=None):
                 super().__init__()
 
                 self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype}
-                # self.factory_kwargs = {"device": device, "dtype": dtype}
 
                 self.in_features = in_features
                 self.out_features = out_features
+                self._orig_shape = (out_features, in_features)
                 if bias:
                     self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs))
                 else:
@@ -1083,151 +1217,12 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
             def reset_parameters(self):
                 return None
 
-            def _load_scale_param(self, state_dict, prefix, param_name, device, manually_loaded_keys, dtype=None):
-                key = f"{prefix}{param_name}"
-                value = state_dict.pop(key, None)
-                if value is not None:
-                    value = value.to(device=device)
-                    if dtype is not None:
-                        value = value.view(dtype=dtype)
-                    manually_loaded_keys.append(key)
-                return value
-
-            def _load_from_state_dict(self, state_dict, prefix, local_metadata,
-                                    strict, missing_keys, unexpected_keys, error_msgs):
-
-                device = self.factory_kwargs["device"]
-                layer_name = prefix.rstrip('.')
-                weight_key = f"{prefix}weight"
-                weight = state_dict.pop(weight_key, None)
-                if weight is None:
-                    logging.warning(f"Missing weight for layer {layer_name}")
-                    self.weight = None
-                    return
-
-                manually_loaded_keys = [weight_key]
-
-                layer_conf = state_dict.pop(f"{prefix}comfy_quant", None)
-                if layer_conf is not None:
-                    layer_conf = json.loads(layer_conf.numpy().tobytes())
-
-                if layer_conf is None:
-                    self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False)
-                else:
-                    self.quant_format = layer_conf.get("format", None)
-                    self._full_precision_mm_config = layer_conf.get("full_precision_matrix_mult", False)
-                    if not self._full_precision_mm:
-                        self._full_precision_mm = self._full_precision_mm_config
-
-                    if self.quant_format in MixedPrecisionOps._disabled:
-                        self._full_precision_mm = True
-
-                    if self.quant_format is None:
-                        raise ValueError(f"Unknown quantization format for layer {layer_name}")
-
-                    qconfig = QUANT_ALGOS[self.quant_format]
-                    self.layout_type = qconfig["comfy_tensor_layout"]
-                    layout_cls = get_layout_class(self.layout_type)
-
-                    # Load format-specific parameters
-                    if self.quant_format in ["float8_e4m3fn", "float8_e5m2"]:
-                        # FP8: single tensor scale
-                        scale = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys)
-
-                        params = layout_cls.Params(
-                            scale=scale,
-                            orig_dtype=MixedPrecisionOps._compute_dtype,
-                            orig_shape=(self.out_features, self.in_features),
-                        )
-
-                    elif self.quant_format == "mxfp8":
-                        # MXFP8: E8M0 block scales stored as uint8 in safetensors
-                        block_scale = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys,
-                                                             dtype=torch.uint8)
-
-                        if block_scale is None:
-                            raise ValueError(f"Missing MXFP8 block scales for layer {layer_name}")
-
-                        block_scale = block_scale.view(torch.float8_e8m0fnu)
-
-                        params = layout_cls.Params(
-                            scale=block_scale,
-                            orig_dtype=MixedPrecisionOps._compute_dtype,
-                            orig_shape=(self.out_features, self.in_features),
-                        )
-
-                    elif self.quant_format == "nvfp4":
-                        # NVFP4: tensor_scale (weight_scale_2) + block_scale (weight_scale)
-                        tensor_scale = self._load_scale_param(state_dict, prefix, "weight_scale_2", device, manually_loaded_keys)
-                        block_scale = self._load_scale_param(state_dict, prefix, "weight_scale", device, manually_loaded_keys,
-                                                             dtype=torch.float8_e4m3fn)
-
-                        if tensor_scale is None or block_scale is None:
-                            raise ValueError(f"Missing NVFP4 scales for layer {layer_name}")
-
-                        params = layout_cls.Params(
-                            scale=tensor_scale,
-                            block_scale=block_scale,
-                            orig_dtype=MixedPrecisionOps._compute_dtype,
-                            orig_shape=(self.out_features, self.in_features),
-                        )
-                    else:
-                        raise ValueError(f"Unsupported quantization format: {self.quant_format}")
-
-                    self.weight = torch.nn.Parameter(
-                        QuantizedTensor(weight.to(device=device, dtype=qconfig["storage_t"]), self.layout_type, params),
-                        requires_grad=False
-                    )
-
-                    for param_name in qconfig["parameters"]:
-                        if param_name in {"weight_scale", "weight_scale_2"}:
-                            continue  # Already handled above
-
-                        param_key = f"{prefix}{param_name}"
-                        _v = state_dict.pop(param_key, None)
-                        if _v is None:
-                            continue
-                        self.register_parameter(param_name, torch.nn.Parameter(_v.to(device=device), requires_grad=False))
-                        manually_loaded_keys.append(param_key)
-
-                super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
-
-                for key in manually_loaded_keys:
-                    if key in missing_keys:
-                        missing_keys.remove(key)
+            def _load_from_state_dict(self, *args):
+                _load_quantized_module(self, super()._load_from_state_dict, *args, load_extra_params=True)
 
             def state_dict(self, *args, destination=None, prefix="", **kwargs):
-                if destination is not None:
-                    sd = destination
-                else:
-                    sd = {}
-
-                if not hasattr(self, 'weight'):
-                    logging.warning("Warning: state dict on uninitialized op {}".format(prefix))
-                    return sd
-
-                if self.bias is not None:
-                    sd["{}bias".format(prefix)] = self.bias
-
-                if self.weight is None:
-                    return sd
-
-                if isinstance(self.weight, QuantizedTensor):
-                    sd_out = self.weight.state_dict("{}weight".format(prefix))
-                    for k in sd_out:
-                        sd[k] = sd_out[k]
-
-                    quant_conf = {"format": self.quant_format}
-                    if self._full_precision_mm_config:
-                        quant_conf["full_precision_matrix_mult"] = True
-                    sd["{}comfy_quant".format(prefix)] = torch.tensor(list(json.dumps(quant_conf).encode('utf-8')), dtype=torch.uint8)
-
-                    input_scale = getattr(self, 'input_scale', None)
-                    if input_scale is not None:
-                        sd["{}input_scale".format(prefix)] = input_scale
-                else:
-                    sd["{}weight".format(prefix)] = self.weight
-                return sd
+                sd = destination if destination is not None else {}
+                return _quantized_weight_state_dict(self, sd, prefix, extra_quant_params=("input_scale",))
 
             def _forward(self, input, weight, bias):
                 return torch.nn.functional.linear(input, weight, bias)
@@ -1317,25 +1312,126 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 self.weight = torch.nn.Parameter(weight, requires_grad=False)
 
             def _apply(self, fn, recurse=True):  # This is to get torch.compile + moving weights to another device working
-                if recurse:
-                    for module in self.children():
-                        module._apply(fn)
+                return _quantized_apply(self, fn, recurse)
 
-                for key, param in self._parameters.items():
-                    if param is None:
-                        continue
-                    p = fn(param)
-                    if (not torch.is_inference_mode_enabled()) and p.is_inference():
-                        p = p.clone()
-                    self.register_parameter(key, torch.nn.Parameter(p, requires_grad=False))
-                for key, buf in self._buffers.items():
-                    if buf is not None:
-                        self._buffers[key] = fn(buf)
-                return self
+        class MoEExperts(torch.nn.Module, CastWeightBiasOp):
+            """Container for E quantized expert weights, indexed via expert_weight(i).
+
+            The bank lives on self.weight as a single 3D tensor — either a
+            compute_dtype Parameter or a Parameter wrapping a QuantizedTensor
+            with leading expert dim.
+
+            State-dict layout matches mixed_precision_ops.Linear with a leading
+            expert dim:
+                {prefix}.weight          quant data (storage_t), leading dim = E
+                {prefix}.weight_scale    block / per-tensor scale
+                {prefix}.weight_scale_2  [E] or scalar           NVFP4 only
+                {prefix}.bias            [E, out_features]       optional, compute_dtype
+                {prefix}.comfy_quant     json -> {{"format": "...", "num_experts": E}}
+
+            Without comfy_quant the weight loads as a plain compute_dtype 3D Parameter [E, out, in].
+            """
+
+            _disabled_formats = disabled
+
+            def __init__(self, num_experts: int, in_features: int, out_features: int, bias: bool = True, device=None, dtype=None):
+                super().__init__()
+                self.num_experts = num_experts
+                self.in_features = in_features
+                self.out_features = out_features
+                self._orig_shape = (num_experts, out_features, in_features)
+                self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype}
+                if bias:
+                    self.bias = torch.nn.Parameter(torch.empty(num_experts, out_features, **self.factory_kwargs))
+                else:
+                    self.register_parameter("bias", None)
+
+                # Populated by _load_from_state_dict:
+                self.weight = None
+                self.quant_format = None
+                self.layout_type = None
+                self._full_precision_mm = MixedPrecisionOps._full_precision_mm
+                self._full_precision_mm_config = False
+                self._resident_bank = None
+
+            def reset_parameters(self):
+                return None
+
+            def _apply(self, fn, recurse=True):
+                return _quantized_apply(self, fn, recurse)
+
+            def _load_from_state_dict(self, *args):
+                _load_quantized_module(self, super()._load_from_state_dict, *args, load_extra_params=False)
+
+            def expert_weight(self, i: int):
+                """Expert i's weight (Tensor or per-expert QuantizedTensor view)."""
+                if isinstance(self.weight, QuantizedTensor):
+                    return self._expert_qt_from(self.weight, i)
+                return self.weight[i]
+
+            @contextlib.contextmanager
+            def bank_resident(self, input):
+                """Cast the whole bank once; expert_linear inside reuses the cast.
+                Not re-entrant — do not nest calls on the same instance.
+                """
+                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+                self._resident_bank = (weight, bias)
+                try:
+                    yield self
+                finally:
+                    self._resident_bank = None
+                    uncast_bias_weight(self, weight, bias, offload_stream)
+
+            def expert_linear(self, input: torch.Tensor, i: int) -> torch.Tensor:
+                """Linear against expert i's weight (with optional bias)."""
+                resident = getattr(self, "_resident_bank", None)
+                if resident is not None:
+                    weight, bias = resident
+                    return self._expert_linear_impl(input, weight, bias, i)
+                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+                try:
+                    return self._expert_linear_impl(input, weight, bias, i)
+                finally:
+                    uncast_bias_weight(self, weight, bias, offload_stream)
+
+            def _expert_linear_impl(self, input, weight, bias, i):
+                if isinstance(weight, QuantizedTensor):
+                    qw = self._expert_qt_from(weight, i)
+                else:
+                    qw = weight[i]
+                b = cast_to_input(bias[i], input, copy=False) if bias is not None else None
+
+                if isinstance(qw, QuantizedTensor):
+                    use_fast = (
+                        not self._full_precision_mm
+                        and qw.layout_cls.supports_fast_matmul()
+                        and input.dim() == 2
+                    )
+                    if use_fast:
+                        qin = QuantizedTensor.from_float(input, self.layout_type)
+                        return torch.nn.functional.linear(qin, qw, b)
+                    out = input @ qw.dequantize().t()
+                    return out + b if b is not None else out
+                return torch.nn.functional.linear(input, qw, b)
+
+            def _expert_qt_from(self, weight: QuantizedTensor, i: int) -> QuantizedTensor:
+                """Build a per-expert QuantizedTensor by indexing into a resident bank."""
+                params = weight._params
+                kwargs = {
+                    "scale": params.scale[i] if params.scale.dim() else params.scale,
+                    "orig_dtype": params.orig_dtype,
+                    "orig_shape": (self.out_features, self.in_features),
+                }
+                if hasattr(params, "block_scale"): # NVFP4
+                    kwargs["block_scale"] = params.block_scale[i]
+                return QuantizedTensor(weight._qdata[i], weight._layout_cls, type(params)(**kwargs))
+
+            def state_dict(self, *args, destination=None, prefix="", **kwargs):
+                sd = destination if destination is not None else {}
+                return _quantized_weight_state_dict(self, sd, prefix, extra_quant_conf={"num_experts": self.num_experts})
 
         class Embedding(manual_cast.Embedding):
-            def _load_from_state_dict(self, state_dict, prefix, local_metadata,
-                                    strict, missing_keys, unexpected_keys, error_msgs):
+            def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
                 weight_key = f"{prefix}weight"
                 layer_conf = state_dict.pop(f"{prefix}comfy_quant", None)
                 if layer_conf is not None:
@@ -1343,14 +1439,16 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
 
                 # Only fp8 makes sense for embeddings (per-row dequant via index select).
                 # Block-scaled formats (NVFP4, MXFP8) can't do per-row lookup efficiently.
-                quant_format = layer_conf.get("format", None) if layer_conf is not None else None
-                if quant_format in ["float8_e4m3fn", "float8_e5m2"] and weight_key in state_dict:
+                quant_format = layer_conf.get("format") if layer_conf is not None else None
+                manually_loaded_keys = []
+
+                if quant_format in ("float8_e4m3fn", "float8_e5m2") and weight_key in state_dict:
                     self.quant_format = quant_format
                     qconfig = QUANT_ALGOS[quant_format]
                     self.layout_type = qconfig["comfy_tensor_layout"]
                     layout_cls = get_layout_class(self.layout_type)
                     weight = state_dict.pop(weight_key)
-                    manually_loaded_keys = [weight_key]
+                    manually_loaded_keys.append(weight_key)
 
                     scale_key = f"{prefix}weight_scale"
                     scale = state_dict.pop(scale_key, None)
@@ -1366,35 +1464,19 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                     self.weight = torch.nn.Parameter(
                         QuantizedTensor(weight.to(dtype=qconfig["storage_t"]), qconfig["comfy_tensor_layout"], params),
                         requires_grad=False)
+                elif layer_conf is not None:
+                    # Unsupported format — restore the marker so it round-trips; fall through to default load.
+                    state_dict[f"{prefix}comfy_quant"] = torch.tensor(
+                        list(json.dumps(layer_conf).encode('utf-8')), dtype=torch.uint8)
 
-                    super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
-                    for k in manually_loaded_keys:
-                        if k in missing_keys:
-                            missing_keys.remove(k)
-                else:
-                    if layer_conf is not None:
-                        state_dict[f"{prefix}comfy_quant"] = torch.tensor(list(json.dumps(layer_conf).encode('utf-8')), dtype=torch.uint8)
-                    super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
+                super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
+                for k in manually_loaded_keys:
+                    if k in missing_keys:
+                        missing_keys.remove(k)
 
             def state_dict(self, *args, destination=None, prefix="", **kwargs):
-                if destination is not None:
-                    sd = destination
-                else:
-                    sd = {}
-
-                if not hasattr(self, 'weight') or self.weight is None:
-                    return sd
-
-                if isinstance(self.weight, QuantizedTensor):
-                    sd_out = self.weight.state_dict("{}weight".format(prefix))
-                    for k in sd_out:
-                        sd[k] = sd_out[k]
-
-                    quant_conf = {"format": self.quant_format}
-                    sd["{}comfy_quant".format(prefix)] = torch.tensor(list(json.dumps(quant_conf).encode('utf-8')), dtype=torch.uint8)
-                else:
-                    sd["{}weight".format(prefix)] = self.weight
-                return sd
+                sd = destination if destination is not None else {}
+                return _quantized_weight_state_dict(self, sd, prefix)
 
             def forward_comfy_cast_weights(self, input, out_dtype=None):
                 weight = self.weight
diff --git a/comfy/sd.py b/comfy/sd.py
index a4e49763a..beb782310 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -68,6 +68,7 @@ import comfy.text_encoders.ernie
 import comfy.text_encoders.gemma4
 import comfy.text_encoders.cogvideo
 import comfy.text_encoders.sa3
+import comfy.text_encoders.gpt_oss
 
 import comfy.model_patcher
 import comfy.lora
@@ -1283,6 +1284,7 @@ class CLIPType(Enum):
     FLUX2 = 25
     LONGCAT_IMAGE = 26
     COGVIDEOX = 27
+    LENS = 28
 
 
 
@@ -1335,6 +1337,7 @@ class TEModel(Enum):
     GEMMA_4_E2B = 30
     GEMMA_4_31B = 31
     T5_GEMMA = 32
+    GPT_OSS_20B = 33
 
 
 def detect_te_model(sd):
@@ -1376,6 +1379,9 @@ def detect_te_model(sd):
             else:
                 return TEModel.GEMMA_3_4B
         return TEModel.GEMMA_2_2B
+    # Must precede the Qwen2.5-7B k_proj.bias=512 check (GPT-OSS also has 8*64=512).
+    if "layers.0.self_attn.sinks" in sd and "layers.0.mlp.experts.gate_up_proj.weight" in sd:
+        return TEModel.GPT_OSS_20B
     if 'model.layers.0.self_attn.k_proj.bias' in sd:
         weight = sd['model.layers.0.self_attn.k_proj.bias']
         if weight.shape[0] == 256:
@@ -1558,6 +1564,10 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             clip_target.clip = comfy.text_encoders.flux.flux2_te(**llama_detect(clip_data), pruned=te_model == TEModel.MISTRAL3_24B_PRUNED_FLUX2)
             clip_target.tokenizer = comfy.text_encoders.flux.Flux2Tokenizer
             tokenizer_data["tekken_model"] = clip_data[0].get("tekken_model", None)
+        elif te_model == TEModel.GPT_OSS_20B:
+            clip_target.clip = comfy.text_encoders.gpt_oss.lens_te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.gpt_oss.LensTokenizer
+            tokenizer_data["tokenizer_json"] = clip_data[0].get("tokenizer_json", None)
         elif te_model == TEModel.QWEN3_4B:
             if clip_type == CLIPType.FLUX or clip_type == CLIPType.FLUX2:
                 clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type="qwen3_4b")
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 617db4f28..e451892e9 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -829,6 +829,48 @@ class Flux2(Flux):
 
         return None
 
+
+class Lens(supported_models_base.BASE):
+    """Microsoft Lens (3.8B dual-stream MMDiT, GPT-OSS-20B text features, Flux2 VAE)."""
+
+    unet_config = {
+        "image_model": "lens",
+    }
+
+    sampling_settings = {
+        "shift": 1.829, # Default mu for 1440x1440 (and any seq_len > 4300
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.Flux2
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float32] # fp16 causes NaNs
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def __init__(self, unet_config):
+        super().__init__(unet_config)
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return model_base.Lens(self, model_type=model_base.ModelType.FLUX, device=device)
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        for hint in ("gpt_oss.transformer.", ""):
+            full_prefix = "{}{}".format(pref, hint)
+            if "{}layers.0.self_attn.sinks".format(full_prefix) in state_dict:
+                detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, full_prefix)
+                return supported_models_base.ClipTarget(
+                    comfy.text_encoders.gpt_oss.LensTokenizer,
+                    comfy.text_encoders.gpt_oss.lens_te(**detect),
+                )
+        return supported_models_base.ClipTarget(
+            comfy.text_encoders.gpt_oss.LensTokenizer,
+            comfy.text_encoders.gpt_oss.lens_te(),
+        )
+
+
 class GenmoMochi(supported_models_base.BASE):
     unet_config = {
         "image_model": "mochi_preview",
@@ -2096,6 +2138,7 @@ models = [
     Omnigen2,
     QwenImage,
     Flux2,
+    Lens,
     Kandinsky5Image,
     Kandinsky5,
     Anima,
diff --git a/comfy/text_encoders/gpt_oss.py b/comfy/text_encoders/gpt_oss.py
new file mode 100644
index 000000000..d596ef9a0
--- /dev/null
+++ b/comfy/text_encoders/gpt_oss.py
@@ -0,0 +1,600 @@
+"""GPT-OSS text encoder for Lens."""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import Any, List, Optional, Sequence
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.ops
+from comfy import sd1_clip
+from comfy.ldm.modules.attention import TORCH_HAS_GQA, optimized_attention_for_device
+from comfy.text_encoders.llama import RMSNorm, apply_rope
+
+
+@dataclass
+class GptOss20BConfig:
+    vocab_size: int = 201088
+    hidden_size: int = 2880
+    intermediate_size: int = 2880
+    num_hidden_layers: int = 24
+    num_attention_heads: int = 64
+    num_key_value_heads: int = 8
+    head_dim: int = 64
+    num_local_experts: int = 32
+    num_experts_per_tok: int = 4
+    sliding_window: int = 128
+    original_max_position_embeddings: int = 4096
+    rope_theta: float = 150000.0
+    rope_factor: float = 32.0
+    rope_beta_fast: float = 32.0
+    rope_beta_slow: float = 1.0
+    rope_truncate: bool = False
+    rms_norm_eps: float = 1e-5
+    attention_bias: bool = True
+    layer_types: Optional[List[str]] = None
+    moe_alpha: float = 1.702
+    moe_limit: float = 7.0
+
+    def __post_init__(self):
+        if self.layer_types is None:
+            self.layer_types = [
+                "sliding_attention" if (i + 1) % 2 else "full_attention"
+                for i in range(self.num_hidden_layers)
+            ]
+
+
+def _yarn_inv_freq(head_dim: int, base: float, factor: float, beta_fast: float, beta_slow: float,
+    original_max_position_embeddings: int, truncate: bool, device=None) -> tuple[torch.Tensor, float]:
+    """YARN inv_freq + attention scaling (matches transformers)."""
+    dim = head_dim
+
+    def find_correction_dim(num_rotations: float) -> float:
+        return (dim * math.log(original_max_position_embeddings / (num_rotations * 2 * math.pi))) / (
+            2 * math.log(base)
+        )
+
+    def find_correction_range() -> tuple[float, float]:
+        low = find_correction_dim(beta_fast)
+        high = find_correction_dim(beta_slow)
+        if truncate:
+            low = math.floor(low)
+            high = math.ceil(high)
+        return max(low, 0), min(high, dim - 1)
+
+    def linear_ramp_factor(min_: float, max_: float, n: int) -> torch.Tensor:
+        if min_ == max_:
+            max_ += 0.001
+        linear = (torch.arange(n, dtype=torch.float32, device=device) - min_) / (max_ - min_)
+        return torch.clamp(linear, 0, 1)
+
+    def get_mscale(scale: float) -> float:
+        if scale <= 1:
+            return 1.0
+        return 0.1 * math.log(scale) + 1.0
+
+    attention_scaling = get_mscale(factor)
+
+    pos_freqs = base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
+    inv_freq_extrapolation = 1.0 / pos_freqs
+    inv_freq_interpolation = 1.0 / (factor * pos_freqs)
+
+    low, high = find_correction_range()
+    extrap_factor = 1 - linear_ramp_factor(low, high, dim // 2)
+    inv_freq = inv_freq_interpolation * (1 - extrap_factor) + inv_freq_extrapolation * extrap_factor
+    return inv_freq, attention_scaling
+
+
+def _build_freqs_cis(inv_freq: torch.Tensor, attention_scaling: float, position_ids: torch.Tensor, dtype: torch.dtype,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    inv_freq_e = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+    pos_e = position_ids[:, None, :].float()
+    freqs = (inv_freq_e @ pos_e).transpose(1, 2)
+    emb = torch.cat((freqs, freqs), dim=-1)
+    cos = (emb.cos() * attention_scaling).to(dtype).unsqueeze(1)
+    sin = (emb.sin() * attention_scaling).to(dtype).unsqueeze(1)
+    sin_split = sin.shape[-1] // 2
+    return cos, sin[..., :sin_split], -sin[..., sin_split:]
+
+
+def _attention_with_sinks(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, sinks: torch.Tensor,
+    attention_mask: Optional[torch.Tensor], num_heads: int, num_kv_groups: int) -> torch.Tensor:
+    """Attention with per-head sinks.
+
+    Sinks add a learned term to each row's softmax denominator but contribute
+    nothing to the output. We fake this by appending one zero k/v position and
+    putting the sink logit in the mask at that column.
+    """
+
+    if num_kv_groups > 1 and not TORCH_HAS_GQA:
+        k = k.repeat_interleave(num_kv_groups, dim=1)
+        v = v.repeat_interleave(num_kv_groups, dim=1)
+
+    B, _, S_q, D = q.shape
+    H_kv = k.shape[1]
+    S_kv = k.shape[-2]
+
+    k = torch.cat([k, k.new_zeros(B, H_kv, 1, D)], dim=-2)
+    v = torch.cat([v, v.new_zeros(B, H_kv, 1, D)], dim=-2)
+
+    sinks_col = sinks.to(q.dtype).view(1, num_heads, 1, 1).expand(B, num_heads, S_q, 1)
+    if attention_mask is not None:
+        mask_left = attention_mask[..., :S_kv].expand(B, num_heads, S_q, S_kv)
+    else:
+        mask_left = q.new_zeros(B, num_heads, S_q, S_kv)
+    mask = torch.cat([mask_left, sinks_col], dim=-1)
+
+    op = optimized_attention_for_device(q.device, mask=True, small_input=True)
+    return op(q, k, v, num_heads, mask=mask, skip_reshape=True, enable_gqa=True)
+
+
+class GptOssAttention(nn.Module):
+    def __init__(self, config: GptOss20BConfig, layer_idx: int, device=None, dtype=None, ops: Any = None):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.layer_type = config.layer_types[layer_idx]
+        self.num_heads = config.num_attention_heads
+        self.num_kv_heads = config.num_key_value_heads
+        self.num_kv_groups = self.num_heads // self.num_kv_heads
+        self.head_dim = config.head_dim
+        self.hidden_size = config.hidden_size
+        self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None
+
+        bias = config.attention_bias
+        self.q_proj = ops.Linear(config.hidden_size, self.num_heads * self.head_dim, bias=bias, device=device, dtype=dtype)
+        self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=bias, device=device, dtype=dtype)
+        self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=bias, device=device, dtype=dtype)
+        self.o_proj = ops.Linear(self.num_heads * self.head_dim, config.hidden_size, bias=bias, device=device, dtype=dtype)
+        self.sinks = nn.Parameter(torch.empty(self.num_heads, device=device, dtype=dtype))
+
+    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor], freqs_cis) -> torch.Tensor:
+        B, S, _ = hidden_states.shape
+
+        q = self.q_proj(hidden_states).view(B, S, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.k_proj(hidden_states).view(B, S, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        v = self.v_proj(hidden_states).view(B, S, self.num_kv_heads, self.head_dim).transpose(1, 2)
+
+        q, k = apply_rope(q, k, freqs_cis)
+
+        out = _attention_with_sinks(q, k, v, self.sinks, attention_mask, self.num_heads, self.num_kv_groups)
+        return self.o_proj(out)
+
+
+# Mixture of Experts
+
+class GptOssTopKRouter(nn.Module):
+    def __init__(self, config: GptOss20BConfig, device=None, dtype=None):
+        super().__init__()
+        self.top_k = config.num_experts_per_tok
+        self.num_experts = config.num_local_experts
+        self.weight = nn.Parameter(torch.empty(config.num_local_experts, config.hidden_size, device=device, dtype=dtype))
+        self.bias = nn.Parameter(torch.empty(config.num_local_experts, device=device, dtype=dtype))
+
+    def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        weight = comfy.ops.cast_to_input(self.weight, hidden_states, copy=False)
+        bias = comfy.ops.cast_to_input(self.bias, hidden_states, copy=False)
+        logits = F.linear(hidden_states, weight, bias)
+        top_vals, top_idx = torch.topk(logits, self.top_k, dim=-1)
+        # Softmax over top-k slice only
+        scores = F.softmax(top_vals, dim=-1, dtype=top_vals.dtype)
+        return scores, top_idx
+
+
+class GptOssExperts(nn.Module):
+    def __init__(self, config: GptOss20BConfig, device=None, dtype=None, ops: Any = None):
+        super().__init__()
+        self.num_experts = config.num_local_experts
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.alpha = config.moe_alpha
+        self.limit = config.moe_limit
+
+        E = self.num_experts
+        H = self.hidden_size
+        I = self.intermediate_size
+
+        self.gate_up_proj = ops.MoEExperts(num_experts=E, in_features=H, out_features=2 * I, bias=True, device=device, dtype=dtype)
+        self.down_proj = ops.MoEExperts(num_experts=E, in_features=I, out_features=H, bias=True, device=device, dtype=dtype)
+
+    def _apply_gate(self, gate_up: torch.Tensor) -> torch.Tensor:
+        gate = gate_up[..., ::2]
+        up = gate_up[..., 1::2]
+        gate = gate.clamp(max=self.limit)
+        up = up.clamp(min=-self.limit, max=self.limit)
+        glu = gate * torch.sigmoid(gate * self.alpha)
+        return torch.addcmul(glu, up, glu)
+
+    def forward(self, hidden_states: torch.Tensor, router_indices: torch.Tensor, routing_weights: torch.Tensor) -> torch.Tensor:
+        N = hidden_states.shape[0]
+        top_k = router_indices.shape[-1]
+        H = hidden_states.shape[-1]
+
+        per_pair = torch.zeros((N * top_k, H), dtype=hidden_states.dtype, device=hidden_states.device)
+
+        expert_mask = F.one_hot(router_indices, num_classes=self.num_experts).permute(2, 1, 0)
+        expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
+
+        with self.gate_up_proj.bank_resident(hidden_states) as gate_up_bank, \
+             self.down_proj.bank_resident(hidden_states) as down_bank:
+            for ei in expert_hit:
+                expert_idx = int(ei.item())
+                top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
+                current = hidden_states[token_idx]
+
+                gate_up = gate_up_bank.expert_linear(current, expert_idx)
+                gated = self._apply_gate(gate_up)
+                expert_out = down_bank.expert_linear(gated, expert_idx)
+
+                weighted = expert_out * routing_weights[token_idx, top_k_pos, None]
+
+                flat_idx = token_idx * top_k + top_k_pos
+                per_pair[flat_idx] = weighted.to(per_pair.dtype)
+
+        return per_pair.view(N, top_k, H).sum(dim=1)
+
+
+class GptOssMLP(nn.Module):
+    def __init__(self, config: GptOss20BConfig, device=None, dtype=None, ops: Any = None):
+        super().__init__()
+        self.router = GptOssTopKRouter(config, device=device, dtype=dtype)
+        self.experts = GptOssExperts(config, device=device, dtype=dtype, ops=ops)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        B, S, H = hidden_states.shape
+        flat = hidden_states.reshape(-1, H)
+        scores, idx = self.router(flat)
+        out = self.experts(flat, idx, scores)
+        return out.reshape(B, S, H)
+
+
+# Decoder layer + model
+
+class GptOssDecoderLayer(nn.Module):
+    def __init__(self, config: GptOss20BConfig, layer_idx: int, device=None, dtype=None, ops: Any = None):
+        super().__init__()
+        self.self_attn = GptOssAttention(config, layer_idx, device=device, dtype=dtype, ops=ops)
+        self.mlp = GptOssMLP(config, device=device, dtype=dtype, ops=ops)
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype)
+        self.layer_type = config.layer_types[layer_idx]
+
+    def forward(self, x: torch.Tensor, attention_masks: dict[str, Optional[torch.Tensor]], freqs_cis) -> torch.Tensor:
+        residual = x
+        x = self.input_layernorm(x)
+        x = self.self_attn(x, attention_masks[self.layer_type], freqs_cis)
+        x = residual + x
+
+        residual = x
+        x = self.post_attention_layernorm(x)
+        x = self.mlp(x)
+        x = residual + x
+        return x
+
+
+def _make_full_causal_mask(B: int, S: int, key_padding_mask: Optional[torch.Tensor], dtype, device):
+    neg = torch.finfo(dtype).min
+    mask = torch.full((S, S), neg, dtype=dtype, device=device).triu_(1)
+    mask = mask.unsqueeze(0).unsqueeze(0).expand(B, 1, S, S).contiguous()
+    if key_padding_mask is not None:
+        kp = key_padding_mask.to(dtype=dtype)
+        kp = (1.0 - kp).reshape(B, 1, 1, S) * neg
+        mask = mask + kp
+    return mask
+
+
+def _make_sliding_causal_mask(B: int, S: int, window: int, key_padding_mask: Optional[torch.Tensor], dtype, device):
+    neg = torch.finfo(dtype).min
+    i = torch.arange(S, device=device).view(-1, 1)
+    j = torch.arange(S, device=device).view(1, -1)
+    keep = (j <= i) & (j > i - window)
+    mask = torch.where(keep, torch.zeros((), dtype=dtype, device=device), torch.full((), neg, dtype=dtype, device=device))
+    mask = mask.unsqueeze(0).unsqueeze(0).expand(B, 1, S, S).contiguous()
+    if key_padding_mask is not None:
+        kp = key_padding_mask.to(dtype=dtype)
+        kp = (1.0 - kp).reshape(B, 1, 1, S) * neg
+        mask = mask + kp
+    return mask
+
+
+class GptOssModel(nn.Module):
+    """GPT-OSS decoder with multi-layer hidden-state capture + early exit."""
+
+    def __init__(self, config: GptOss20BConfig, device=None, dtype=None, ops: Any = None):
+        super().__init__()
+        self.config = config
+        self.dtype = dtype
+        self.embed_tokens = ops.Embedding(config.vocab_size, config.hidden_size, device=device, dtype=dtype)
+        self.layers = nn.ModuleList(
+            [
+                GptOssDecoderLayer(config, i, device=device, dtype=dtype, ops=ops)
+                for i in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype)
+
+        # Always build on CPU so the buffer survives meta-device construction.
+        inv_freq, attn_scaling = _yarn_inv_freq(
+            head_dim=config.head_dim,
+            base=config.rope_theta,
+            factor=config.rope_factor,
+            beta_fast=config.rope_beta_fast,
+            beta_slow=config.rope_beta_slow,
+            original_max_position_embeddings=config.original_max_position_embeddings,
+            truncate=config.rope_truncate,
+            device=torch.device("cpu"),
+        )
+        self.register_buffer("rope_inv_freq", inv_freq, persistent=False)
+        self.rope_attention_scaling = float(attn_scaling)
+
+    @property
+    def num_layers(self) -> int:
+        return self.config.num_hidden_layers
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def _build_attention_masks(self, B: int, S: int, attention_mask: Optional[torch.Tensor], dtype: torch.dtype, device,
+    ) -> dict[str, torch.Tensor]:
+        full = _make_full_causal_mask(B, S, attention_mask, dtype, device)
+        masks = {"full_attention": full}
+        if any(t == "sliding_attention" for t in self.config.layer_types):
+            masks["sliding_attention"] = _make_sliding_causal_mask(
+                B, S, self.config.sliding_window, attention_mask, dtype, device
+            )
+        return masks
+
+    def forward(self, input_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None,
+                capture_layers: Optional[Sequence[int]] = None) -> dict[str, Any]:
+        B, S = input_ids.shape
+        device = input_ids.device
+        dtype = self.dtype
+
+        hidden_states = self.embed_tokens(input_ids, out_dtype=dtype)
+
+        position_ids = torch.arange(S, device=device).unsqueeze(0).expand(B, -1)
+        freqs_cis = _build_freqs_cis(self.rope_inv_freq.to(device=device), self.rope_attention_scaling, position_ids, dtype)
+
+        attn_masks = self._build_attention_masks(B, S, attention_mask, dtype, device)
+
+        capture_layers = list(capture_layers) if capture_layers else None
+        if capture_layers:
+            max_layer = max(capture_layers)
+            wanted = {idx: pos for pos, idx in enumerate(capture_layers)}
+            captured: List[Optional[torch.Tensor]] = [None] * len(capture_layers)
+        else:
+            max_layer = self.config.num_hidden_layers - 1
+            wanted = None
+            captured = None
+
+        for i, layer in enumerate(self.layers):
+            hidden_states = layer(hidden_states, attn_masks, freqs_cis)
+            if wanted is not None and i in wanted:
+                captured[wanted[i]] = hidden_states
+            if i >= max_layer:
+                break
+
+        if captured is not None:
+            return {"hidden_states": captured}
+        return {"last_hidden_state": self.norm(hidden_states)}
+
+
+# Lens chat-template constants (verbatim from the reference pipeline).
+_LENS_CHAT_SYSTEM = (
+    "Describe the image by detailing the color, shape, size, texture, "
+    "quantity, text, spatial relationships of the objects and background."
+)
+_LENS_CHAT_ASSISTANT_THINKING = "Need to generate one image according to the description."
+LENS_TXT_OFFSET = 97
+LENS_SELECTED_LAYERS = (5, 11, 17, 23)
+LENS_MAX_TOKENS = 512
+
+
+# The reference GPT-OSS Harmony template injects today's date here
+_LENS_CHAT_DATE = "2026-05-23"
+
+
+def _lens_render_chat(prompt: str) -> str:
+    """Render the Lens prompt in GPT-OSS Harmony format."""
+    return (
+        f"<|start|>system<|message|>"
+        f"You are ChatGPT, a large language model trained by OpenAI.\n"
+        f"Knowledge cutoff: 2024-06\n"
+        f"Current date: {_LENS_CHAT_DATE}\n\n"
+        f"Reasoning: medium\n\n"
+        f"# Valid channels: analysis, commentary, final. "
+        f"Channel must be included for every message.<|end|>"
+        f"<|start|>developer<|message|># Instructions\n\n"
+        f"{_LENS_CHAT_SYSTEM}\n\n<|end|>"
+        f"<|start|>user<|message|>{prompt}<|end|>"
+        f"<|start|>assistant<|channel|>analysis<|message|>"
+        f"{_LENS_CHAT_ASSISTANT_THINKING}<|end|>"
+        f"<|start|>assistant<|channel|>final<|message|>"
+    )
+
+
+# GPT-OSS-20B fixed token IDs (from the tokenizer's added-tokens table).
+_LENS_PAD_TOKEN_ID = 199999  # <|endoftext|>
+
+
+class _GptOssRawTokenizer:
+    """Raw ``tokenizers.Tokenizer`` wrapper.
+
+    The tokenizer JSON ships as a byte tensor inside the encoder checkpoint
+    (``tokenizer_json`` key) rather than as a committed file. Extracted
+    it in ``sd.py`` and passes it here via ``tokenizer_data``.
+    """
+
+    def __init__(self, tokenizer_json_bytes=None, **kwargs):
+        from tokenizers import Tokenizer
+        if isinstance(tokenizer_json_bytes, torch.Tensor):
+            tokenizer_json_bytes = bytes(tokenizer_json_bytes.tolist())
+        if tokenizer_json_bytes is None:
+            raise ValueError(
+                "Lens tokenizer requires the ``tokenizer_json`` byte tensor in the "
+                "encoder state dict. Re-bundle the encoder via bundle_te.py so it "
+                "embeds the tokenizer."
+            )
+        self.tokenizer = Tokenizer.from_str(tokenizer_json_bytes.decode("utf-8"))
+
+    @classmethod
+    def from_pretrained(cls, tokenizer_data, **kwargs):
+        return cls(tokenizer_json_bytes=tokenizer_data, **kwargs)
+
+    def __call__(self, text):
+        return {"input_ids": self.tokenizer.encode(text, add_special_tokens=False).ids}
+
+    def get_vocab(self):
+        return self.tokenizer.get_vocab()
+
+    def convert_tokens_to_ids(self, tokens):
+        return [self.tokenizer.token_to_id(t) for t in tokens]
+
+    def decode(self, ids, **kwargs):
+        return self.tokenizer.decode(ids, skip_special_tokens=kwargs.get("skip_special_tokens", False))
+
+
+class LensGptOssTokenizer(sd1_clip.SDTokenizer):
+    tokenizer_json_data = None
+
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_json = tokenizer_data.get("tokenizer_json", None)
+        self.tokenizer_json_data = tokenizer_json
+        super().__init__(
+            tokenizer_json,
+            embedding_directory=embedding_directory,
+            pad_with_end=False,
+            embedding_size=2880,
+            embedding_key="gpt_oss",
+            tokenizer_class=_GptOssRawTokenizer,
+            has_start_token=False,
+            has_end_token=False,
+            pad_to_max_length=False,
+            max_length=99999999,
+            min_length=1,
+            pad_left=False,
+            disable_weights=True,
+            tokenizer_data=tokenizer_data,
+        )
+        self.pad_token_id = _LENS_PAD_TOKEN_ID
+
+    def tokenize_with_weights(self, text: str, return_word_ids=False, **kwargs):
+        # Empty prompt -> empty list; encode_token_weights returns zeros (uncond).
+        if not text or not text.strip():
+            return [[]]
+        rendered = _lens_render_chat(text)
+        ids = self.tokenizer(rendered)["input_ids"]
+        if len(ids) > LENS_MAX_TOKENS:
+            ids = ids[:LENS_MAX_TOKENS]
+        return [[(int(t), 1.0) for t in ids]]
+
+    def state_dict(self):
+        if self.tokenizer_json_data is not None:
+            return {"tokenizer_json": self.tokenizer_json_data}
+        return {}
+
+
+class LensTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(
+            embedding_directory=embedding_directory,
+            tokenizer_data=tokenizer_data,
+            name="gpt_oss",
+            tokenizer=LensGptOssTokenizer,
+        )
+
+
+class LensGptOssClipModel(nn.Module):
+    """SDClipModel-shaped Lens GPT-OSS encoder (multi-layer feature extractor)."""
+
+    def __init__(self, device="cpu", dtype=None, model_options=None, **kwargs):
+        super().__init__()
+        model_options = dict(model_options or {})
+
+        operations = model_options.get("custom_operations")
+        if operations is None:
+            quant_config = model_options.get("quantization_metadata") or {}
+            operations = comfy.ops.mixed_precision_ops(quant_config, dtype, full_precision_mm=True)
+        self.operations = operations
+
+        cfg_overrides = model_options.get("gpt_oss_config", {})
+        self.config = GptOss20BConfig(**cfg_overrides)
+        self.selected_layers = tuple(model_options.get("selected_layers", LENS_SELECTED_LAYERS))
+        self.txt_offset = int(model_options.get("txt_offset", LENS_TXT_OFFSET))
+
+        self.transformer = GptOssModel(self.config, device=device, dtype=dtype, ops=operations)
+        self.num_layers = self.config.num_hidden_layers
+        self.dtype = dtype
+        self.execution_device = None
+        self._pad_token_id = _LENS_PAD_TOKEN_ID
+
+    def set_clip_options(self, options):
+        self.execution_device = options.get("execution_device", self.execution_device)
+
+    def reset_clip_options(self):
+        self.execution_device = None
+
+    def _gather_tokens(self, token_weight_pairs):
+        ids_list = [[int(t[0]) for t in batch] for batch in token_weight_pairs]
+        pad_id = self._pad_token_id
+        max_len = max(len(x) for x in ids_list)
+        device = self.execution_device
+        ids = torch.full((len(ids_list), max_len), pad_id, dtype=torch.long, device=device)
+        mask = torch.zeros((len(ids_list), max_len), dtype=torch.long, device=device)
+        for i, x in enumerate(ids_list):
+            ids[i, : len(x)] = torch.tensor(x, dtype=torch.long, device=device)
+            mask[i, : len(x)] = 1
+        return ids, mask
+
+    def encode_token_weights(self, token_weight_pairs):
+        # Empty negative: emit zero-length features + zero mask
+        if all(len(batch) == 0 for batch in token_weight_pairs):
+            device = self.execution_device
+            B = len(token_weight_pairs)
+            L = len(self.selected_layers)
+            H = self.config.hidden_size
+            flat = torch.zeros(B, 0, L * H, dtype=self.dtype, device=device)
+            mask = torch.zeros(B, 0, dtype=torch.long, device=device)
+            return flat, None, {"attention_mask": mask, "num_layers_stacked": L}
+
+        input_ids, attn_mask = self._gather_tokens(token_weight_pairs)
+        out = self.transformer(input_ids, attention_mask=attn_mask, capture_layers=self.selected_layers)
+        layers = out["hidden_states"]  # list of L × [B, S, H]
+        stacked = torch.stack(layers, dim=2)  # [B, S, L, H]
+
+        offset = self.txt_offset
+        if stacked.shape[1] > offset:
+            stacked = stacked[:, offset:].contiguous()
+            mask_trim = attn_mask[:, offset:]
+        else:
+            stacked = stacked[:, :0]
+            mask_trim = attn_mask[:, :0]
+
+        B, S, L, H = stacked.shape
+        flat = stacked.reshape(B, S, L * H)
+        extra = {"attention_mask": mask_trim, "num_layers_stacked": L}
+        return flat, None, extra
+
+    def load_sd(self, sd):
+        return self.transformer.load_state_dict(sd, strict=False, assign=True)
+
+
+class LensTEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options=None):
+        super().__init__(device=device, dtype=dtype, name="gpt_oss", clip_model=LensGptOssClipModel, model_options=model_options or {})
+
+
+def lens_te(dtype_llama=None, llama_quantization_metadata=None):
+    class LensTEModel_(LensTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options=None):
+            mo = dict(model_options or {})
+            if llama_quantization_metadata is not None:
+                mo["quantization_metadata"] = llama_quantization_metadata
+            if dtype is None and dtype_llama is not None:
+                dtype = dtype_llama
+            super().__init__(device=device, dtype=dtype, model_options=mo)
+
+    return LensTEModel_
diff --git a/comfy_extras/nodes_cfg.py b/comfy_extras/nodes_cfg.py
index 4ebb4b51e..b585c560f 100644
--- a/comfy_extras/nodes_cfg.py
+++ b/comfy_extras/nodes_cfg.py
@@ -57,24 +57,55 @@ class CFGNorm(io.ComfyNode):
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01),
+                io.Boolean.Input(
+                    "pre_cfg",
+                    default=False,
+                    optional=True,
+                    tooltip=(
+                        "If true, rescale the combined noise BEFORE the sampler's CFG combine, "
+                        "without clamping (can amplify). Matches the norm-scaled CFG used by "
+                        "models like Lens. Default false keeps the original post-CFG x0-space "
+                        "attenuate-only behavior."
+                    ),
+                ),
             ],
             outputs=[io.Model.Output(display_name="patched_model")],
             is_experimental=True,
         )
 
     @classmethod
-    def execute(cls, model, strength) -> io.NodeOutput:
+    def execute(cls, model, strength, pre_cfg=False) -> io.NodeOutput:
         m = model.clone()
-        def cfg_norm(args):
-            cond_p = args['cond_denoised']
-            pred_text_ = args["denoised"]
+        if pre_cfg:
+            def cfg_norm_pre(args):
+                cond = args["cond"]
+                uncond = args["uncond"]
+                cond_scale = args["cond_scale"]
+                comb = uncond + cond_scale * (cond - uncond)
+                cond_norm = torch.linalg.vector_norm(cond, dim=1, keepdim=True)
+                comb_norm = torch.linalg.vector_norm(comb, dim=1, keepdim=True)
+                rescale = torch.where(
+                    comb_norm > 0,
+                    cond_norm / comb_norm.clamp_min(1e-12),
+                    torch.ones_like(comb_norm),
+                )
+                rescaled = comb * rescale
+                # strength blends back toward standard linear CFG (1.0 = full rescale).
+                if strength != 1.0:
+                    rescaled = strength * rescaled + (1.0 - strength) * comb
+                return rescaled
+            m.set_model_sampler_cfg_function(cfg_norm_pre)
+        else:
+            def cfg_norm(args):
+                cond_p = args['cond_denoised']
+                pred_text_ = args["denoised"]
 
-            norm_full_cond = torch.norm(cond_p, dim=1, keepdim=True)
-            norm_pred_text = torch.norm(pred_text_, dim=1, keepdim=True)
-            scale = (norm_full_cond / (norm_pred_text + 1e-8)).clamp(min=0.0, max=1.0)
-            return pred_text_ * scale * strength
+                norm_full_cond = torch.norm(cond_p, dim=1, keepdim=True)
+                norm_pred_text = torch.norm(pred_text_, dim=1, keepdim=True)
+                scale = (norm_full_cond / (norm_pred_text + 1e-8)).clamp(min=0.0, max=1.0)
+                return pred_text_ * scale * strength
 
-        m.set_model_sampler_post_cfg_function(cfg_norm)
+            m.set_model_sampler_post_cfg_function(cfg_norm)
         return io.NodeOutput(m)
 
 
diff --git a/nodes.py b/nodes.py
index 669a7057b..13d3864cd 100644
--- a/nodes.py
+++ b/nodes.py
@@ -969,7 +969,7 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
-                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image", "cogvideox"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image", "cogvideox", "lens"], ),
                               },
                 "optional": {
                               "device": (["default", "cpu"], {"advanced": True}),
@@ -979,7 +979,7 @@ class CLIPLoader:
 
     CATEGORY = "advanced/loaders"
 
-    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B"
+    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B\nlens: gpt-oss-20b"
 
     def load_clip(self, clip_name, type="stable_diffusion", device="default"):
         clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)