Merge 53c83214ef into 0904cc3fe5

comfy/sd.py

@@ -61,6 +61,7 @@ import comfy.text_encoders.newbie
 import comfy.text_encoders.anima
 import comfy.text_encoders.ace15
 import comfy.text_encoders.longcat_image
+import comfy.text_encoders.qwen35
 
 import comfy.model_patcher
 import comfy.lora
@@ -425,13 +426,13 @@ class CLIP:
     def get_key_patches(self):
         return self.patcher.get_key_patches()
 
-    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):
+    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None, presence_penalty=0.0):
         self.cond_stage_model.reset_clip_options()
 
         self.load_model(tokens)
         self.cond_stage_model.set_clip_options({"layer": None})
         self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
-        return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)
+        return self.cond_stage_model.generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed, presence_penalty=presence_penalty)
 
     def decode(self, token_ids, skip_special_tokens=True):
         return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
@@ -1210,6 +1211,11 @@ class TEModel(Enum):
     QWEN3_8B = 20
     QWEN3_06B = 21
     GEMMA_3_4B_VISION = 22
+    QWEN35_08B = 23
+    QWEN35_2B = 24
+    QWEN35_4B = 25
+    QWEN35_9B = 26
+    QWEN35_27B = 27
 
 
 def detect_te_model(sd):
@@ -1249,6 +1255,17 @@ def detect_te_model(sd):
             return TEModel.QWEN25_3B
         if weight.shape[0] == 512:
             return TEModel.QWEN25_7B
+    if "model.language_model.layers.0.linear_attn.A_log" in sd and "model.language_model.layers.0.input_layernorm.weight" in sd:
+        weight = sd['model.language_model.layers.0.input_layernorm.weight']
+        if weight.shape[0] == 1024:
+            return TEModel.QWEN35_08B
+        if weight.shape[0] == 2560:
+            return TEModel.QWEN35_4B
+        if weight.shape[0] == 4096:
+            return TEModel.QWEN35_9B
+        if weight.shape[0] == 5120:
+            return TEModel.QWEN35_27B
+        return TEModel.QWEN35_2B
     if "model.layers.0.post_attention_layernorm.weight" in sd:
         weight = sd['model.layers.0.post_attention_layernorm.weight']
         if 'model.layers.0.self_attn.q_norm.weight' in sd:
@@ -1281,11 +1298,12 @@ def t5xxl_detect(clip_data):
     return {}
 
 def llama_detect(clip_data):
-    weight_name = "model.layers.0.self_attn.k_proj.weight"
+    weight_names = ["model.layers.0.self_attn.k_proj.weight", "model.layers.0.linear_attn.in_proj_a.weight"]
 
     for sd in clip_data:
-        if weight_name in sd:
-            return comfy.text_encoders.hunyuan_video.llama_detect(sd)
+        for weight_name in weight_names:
+            if weight_name in sd:
+                return comfy.text_encoders.hunyuan_video.llama_detect(sd)
 
     return {}
 
@@ -1413,6 +1431,11 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         elif te_model == TEModel.JINA_CLIP_2:
             clip_target.clip = comfy.text_encoders.jina_clip_2.JinaClip2TextModelWrapper
             clip_target.tokenizer = comfy.text_encoders.jina_clip_2.JinaClip2TokenizerWrapper
+        elif te_model in (TEModel.QWEN35_08B, TEModel.QWEN35_2B, TEModel.QWEN35_4B, TEModel.QWEN35_9B, TEModel.QWEN35_27B):
+            clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
+            qwen35_type = {TEModel.QWEN35_08B: "qwen35_08b", TEModel.QWEN35_2B: "qwen35_2b", TEModel.QWEN35_4B: "qwen35_4b", TEModel.QWEN35_9B: "qwen35_9b", TEModel.QWEN35_27B: "qwen35_27b"}[te_model]
+            clip_target.clip = comfy.text_encoders.qwen35.te(**llama_detect(clip_data), model_type=qwen35_type)
+            clip_target.tokenizer = comfy.text_encoders.qwen35.tokenizer(model_type=qwen35_type)
         elif te_model == TEModel.QWEN3_06B:
             clip_target.clip = comfy.text_encoders.anima.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.anima.AnimaTokenizer

comfy/sd1_clip.py

@@ -308,14 +308,14 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     def load_sd(self, sd):
         return self.transformer.load_state_dict(sd, strict=False, assign=getattr(self, "can_assign_sd", False))
 
-    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed):
+    def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty=0.0):
         if isinstance(tokens, dict):
             tokens_only = next(iter(tokens.values())) # todo: get this better?
         else:
             tokens_only = tokens
         tokens_only = [[t[0] for t in b] for b in tokens_only]
         embeds = self.process_tokens(tokens_only, device=self.execution_device)[0]
-        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed)
+        return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty=presence_penalty)
 
 def parse_parentheses(string):
     result = []
@@ -740,5 +740,5 @@ class SD1ClipModel(torch.nn.Module):
     def load_sd(self, sd):
         return getattr(self, self.clip).load_sd(sd)
 
-    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None):
-        return getattr(self, self.clip).generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed)
+    def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None, presence_penalty=0.0):
+        return getattr(self, self.clip).generate(tokens, do_sample=do_sample, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, min_p=min_p, repetition_penalty=repetition_penalty, seed=seed, presence_penalty=presence_penalty)

comfy/text_encoders/llama.py

@@ -655,6 +655,17 @@ class Llama2_(nn.Module):
         if config.lm_head:
             self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
 
+    def get_past_len(self, past_key_values):
+        return past_key_values[0][2]
+
+    def compute_freqs_cis(self, position_ids, device):
+        return precompute_freqs_cis(self.config.head_dim,
+                                    position_ids,
+                                    self.config.rope_theta,
+                                    self.config.rope_scale,
+                                    self.config.rope_dims,
+                                    device=device)
+
     def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None):
         if embeds is not None:
             x = embeds
@@ -667,17 +678,12 @@ class Llama2_(nn.Module):
         seq_len = x.shape[1]
         past_len = 0
         if past_key_values is not None and len(past_key_values) > 0:
-            past_len = past_key_values[0][2]
+            past_len = self.get_past_len(past_key_values)
 
         if position_ids is None:
             position_ids = torch.arange(past_len, past_len + seq_len, device=x.device).unsqueeze(0)
 
-        freqs_cis = precompute_freqs_cis(self.config.head_dim,
-                                         position_ids,
-                                         self.config.rope_theta,
-                                         self.config.rope_scale,
-                                         self.config.rope_dims,
-                                         device=x.device)
+        freqs_cis = self.compute_freqs_cis(position_ids, x.device)
 
         mask = None
         if attention_mask is not None:
@@ -812,9 +818,16 @@ class BaseGenerate:
         comfy.ops.uncast_bias_weight(module, weight, None, offload_stream)
         return x
 
-    def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0):
-        device = embeds.device
+    def init_kv_cache(self, batch, max_cache_len, device, execution_dtype):
         model_config = self.model.config
+        past_key_values = []
+        for x in range(model_config.num_hidden_layers):
+            past_key_values.append((torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),
+                                    torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0))
+        return past_key_values
+
+    def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0, presence_penalty=0.0):
+        device = embeds.device
 
         if stop_tokens is None:
             stop_tokens = self.model.config.stop_tokens
@@ -829,11 +842,8 @@ class BaseGenerate:
         if embeds.ndim == 2:
             embeds = embeds.unsqueeze(0)
 
-        past_key_values = [] #kv_cache init
         max_cache_len = embeds.shape[1] + max_length
-        for x in range(model_config.num_hidden_layers):
-            past_key_values.append((torch.empty([embeds.shape[0], model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),
-                                    torch.empty([embeds.shape[0], model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0))
+        past_key_values = self.init_kv_cache(embeds.shape[0], max_cache_len, device, execution_dtype)
 
         generator = torch.Generator(device=device).manual_seed(seed) if do_sample else None
 
@@ -844,7 +854,7 @@ class BaseGenerate:
         for step in tqdm(range(max_length), desc="Generating tokens"):
             x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values)
             logits = self.logits(x)[:, -1]
-            next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample)
+            next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample, presence_penalty=presence_penalty)
             token_id = next_token[0].item()
             generated_token_ids.append(token_id)
 
@@ -856,7 +866,7 @@ class BaseGenerate:
 
         return generated_token_ids
 
-    def sample_token(self, logits, temperature, top_k, top_p, min_p, repetition_penalty, token_history, generator, do_sample=True):
+    def sample_token(self, logits, temperature, top_k, top_p, min_p, repetition_penalty, token_history, generator, do_sample=True, presence_penalty=0.0):
 
         if not do_sample or temperature == 0.0:
             return torch.argmax(logits, dim=-1, keepdim=True)
@@ -867,6 +877,11 @@ class BaseGenerate:
                 for token_id in set(token_history):
                     logits[i, token_id] *= repetition_penalty if logits[i, token_id] < 0 else 1/repetition_penalty
 
+        if presence_penalty is not None and presence_penalty != 0.0:
+            for i in range(logits.shape[0]):
+                for token_id in set(token_history):
+                    logits[i, token_id] -= presence_penalty
+
         if temperature != 1.0:
             logits = logits / temperature
 

comfy/text_encoders/qwen35.py

@@ -0,0 +1,833 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dataclasses import dataclass, field
+import os
+import math
+
+import comfy.model_management
+from comfy.ldm.modules.attention import optimized_attention_for_device
+from comfy import sd1_clip
+import comfy.text_encoders.qwen_vl
+
+from .llama import BaseLlama, BaseGenerate, Llama2_, MLP, RMSNorm, apply_rope
+
+
+def _qwen35_layer_types(n):
+    return [("full_attention" if (i + 1) % 4 == 0 else "linear_attention") for i in range(n)]
+
+@dataclass
+class Qwen35Config:
+    vocab_size: int = 248320
+    hidden_size: int = 2048
+    intermediate_size: int = 6144
+    num_hidden_layers: int = 24
+    # Full attention params
+    num_attention_heads: int = 8
+    num_key_value_heads: int = 2
+    head_dim: int = 256
+    partial_rotary_factor: float = 0.25
+    # Linear attention (DeltaNet) params
+    linear_num_key_heads: int = 16
+    linear_num_value_heads: int = 16
+    linear_key_head_dim: int = 128
+    linear_value_head_dim: int = 128
+    conv_kernel_size: int = 4
+    # Shared params
+    max_position_embeddings: int = 32768
+    rms_norm_eps: float = 1e-6
+    rope_theta: float = 10000000.0
+    mrope_section: list = field(default_factory=lambda: [11, 11, 10])
+    layer_types: list = field(default_factory=lambda: _qwen35_layer_types(24))
+    rms_norm_add: bool = True
+    mlp_activation: str = "silu"
+    qkv_bias: bool = False
+    final_norm: bool = True
+    lm_head: bool = False
+    stop_tokens: list = field(default_factory=lambda: [248044, 248046])
+    # These are needed for BaseLlama/BaseGenerate compatibility but unused directly
+    transformer_type: str = "qwen35_2b"
+    rope_dims: list = None
+    rope_scale: float = None
+
+QWEN35_VISION_DEFAULTS = dict(hidden_size=1024, num_heads=16, intermediate_size=4096, depth=24, patch_size=16, temporal_patch_size=2, in_channels=3, spatial_merge_size=2, num_position_embeddings=2304)
+
+QWEN35_MODELS = {
+    "qwen35_08b": dict(hidden_size=1024, intermediate_size=3584, vision=dict(hidden_size=768, num_heads=12, intermediate_size=3072, depth=12)),
+    "qwen35_2b": dict(hidden_size=2048, intermediate_size=6144, num_hidden_layers=24, num_attention_heads=8, num_key_value_heads=2, linear_num_value_heads=16),
+    "qwen35_4b": dict(hidden_size=2560, intermediate_size=9216, num_hidden_layers=32, num_attention_heads=16, num_key_value_heads=4, linear_num_value_heads=32),
+    "qwen35_9b": dict(hidden_size=4096, intermediate_size=12288, num_hidden_layers=32, num_attention_heads=16, num_key_value_heads=4, linear_num_value_heads=32, lm_head=True, vision=dict(hidden_size=1152, intermediate_size=4304, depth=27)),
+    "qwen35_27b": dict(hidden_size=5120, intermediate_size=17408, num_hidden_layers=64, num_attention_heads=24, num_key_value_heads=4, linear_num_value_heads=48, lm_head=True, vision=dict(hidden_size=1152, intermediate_size=4304, depth=27)),
+}
+
+
+def _make_config(model_type, config_dict={}):
+    overrides = QWEN35_MODELS.get(model_type, {}).copy()
+    overrides.pop("vision", None)
+    if "num_hidden_layers" in overrides:
+        overrides["layer_types"] = _qwen35_layer_types(overrides["num_hidden_layers"])
+    overrides.update(config_dict)
+    return Qwen35Config(**overrides)
+
+
+class RMSNormGated(RMSNorm):
+    def forward(self, x, gate):
+        return super().forward(x) * F.silu(gate.to(x.dtype))
+
+def torch_chunk_gated_delta_rule(query, key, value, g, beta, chunk_size=64, initial_state=None, output_final_state=False):
+    initial_dtype = query.dtype
+    query = F.normalize(query, dim=-1)
+    key = F.normalize(key, dim=-1)
+    query, key, value, beta, g = [x.transpose(1, 2).contiguous().to(torch.float32) for x in (query, key, value, beta, g)]
+
+    batch_size, num_heads, sequence_length, k_head_dim = key.shape
+    v_head_dim = value.shape[-1]
+    pad_size = (chunk_size - sequence_length % chunk_size) % chunk_size
+    query = F.pad(query, (0, 0, 0, pad_size))
+    key = F.pad(key, (0, 0, 0, pad_size))
+    value = F.pad(value, (0, 0, 0, pad_size))
+    beta = F.pad(beta, (0, pad_size))
+    g = F.pad(g, (0, pad_size))
+    total_sequence_length = sequence_length + pad_size
+    scale = 1 / (query.shape[-1] ** 0.5)
+    query = query * scale
+
+    v_beta = value * beta.unsqueeze(-1)
+    k_beta = key * beta.unsqueeze(-1)
+    query, key, value, k_beta, v_beta = [x.reshape(x.shape[0], x.shape[1], -1, chunk_size, x.shape[-1]) for x in (query, key, value, k_beta, v_beta)]
+    g = g.reshape(g.shape[0], g.shape[1], -1, chunk_size)
+    mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), diagonal=0)
+
+    g = g.cumsum(dim=-1)
+    decay_mask = ((g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().float()).tril()
+    attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask, 0)
+    for i in range(1, chunk_size):
+        row = attn[..., i, :i].clone()
+        sub = attn[..., :i, :i].clone()
+        attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
+    attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
+    value = attn @ v_beta
+    k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
+    last_recurrent_state = (
+        torch.zeros(batch_size, num_heads, k_head_dim, v_head_dim).to(value)
+        if initial_state is None
+        else initial_state.to(value)
+    )
+    core_attn_out = torch.zeros_like(value)
+    mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), diagonal=1)
+
+    for i in range(0, total_sequence_length // chunk_size):
+        q_i, k_i, v_i = query[:, :, i], key[:, :, i], value[:, :, i]
+        attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
+        v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
+        v_new = v_i - v_prime
+        attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
+        core_attn_out[:, :, i] = attn_inter + attn @ v_new
+        last_recurrent_state = (
+            last_recurrent_state * g[:, :, i, -1, None, None].exp()
+            + (k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]).transpose(-1, -2) @ v_new
+        )
+
+    if not output_final_state:
+        last_recurrent_state = None
+    core_attn_out = core_attn_out.reshape(core_attn_out.shape[0], core_attn_out.shape[1], -1, core_attn_out.shape[-1])
+    core_attn_out = core_attn_out[:, :, :sequence_length]
+    core_attn_out = core_attn_out.transpose(1, 2).contiguous().to(initial_dtype)
+    return core_attn_out, last_recurrent_state
+
+
+def torch_causal_conv1d_update(x, conv_state, weight, bias=None):
+    # conv_state: [B, channels, kernel_size-1], x: [B, channels, 1]
+    # weight: [channels, kernel_size]
+    state_len = conv_state.shape[-1]
+    combined = torch.cat([conv_state, x], dim=-1).to(weight.dtype)  # [B, channels, kernel_size]
+    conv_state.copy_(combined[:, :, -state_len:])
+    out = (combined * weight).sum(dim=-1, keepdim=True)  # [B, channels, 1]
+    if bias is not None:
+        out = out + bias.unsqueeze(0).unsqueeze(-1)
+    return F.silu(out).to(x.dtype)
+
+
+# GatedDeltaNet - Linear Attention Layer
+
+class GatedDeltaNet(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+
+        hidden = config.hidden_size
+        self.num_key_heads = config.linear_num_key_heads
+        self.num_value_heads = config.linear_num_value_heads
+        self.key_head_dim = config.linear_key_head_dim
+        self.value_head_dim = config.linear_value_head_dim
+        self.conv_kernel_size = config.conv_kernel_size
+
+        key_dim = self.num_key_heads * self.key_head_dim
+        value_dim = self.num_value_heads * self.value_head_dim
+        self.key_dim = key_dim
+        self.value_dim = value_dim
+        conv_dim = key_dim * 2 + value_dim
+
+        self.in_proj_qkv = ops.Linear(hidden, conv_dim, bias=False, device=device, dtype=dtype)
+        self.in_proj_z = ops.Linear(hidden, value_dim, bias=False, device=device, dtype=dtype)
+        self.in_proj_b = ops.Linear(hidden, self.num_value_heads, bias=False, device=device, dtype=dtype)
+        self.in_proj_a = ops.Linear(hidden, self.num_value_heads, bias=False, device=device, dtype=dtype)
+        self.out_proj = ops.Linear(value_dim, hidden, bias=False, device=device, dtype=dtype)
+
+        self.dt_bias = nn.Parameter(torch.empty(self.num_value_heads, device=device, dtype=dtype))
+        self.A_log = nn.Parameter(torch.empty(self.num_value_heads, device=device, dtype=dtype))
+
+        self.conv1d = ops.Conv1d(in_channels=conv_dim, out_channels=conv_dim, bias=False, kernel_size=self.conv_kernel_size,
+            groups=conv_dim, padding=self.conv_kernel_size - 1, device=device, dtype=dtype)
+
+        self.norm = RMSNormGated(self.value_head_dim, eps=config.rms_norm_eps, device=device, dtype=dtype)
+
+    def forward(self, x, past_key_value=None, **kwargs):
+        batch_size, seq_len, _ = x.shape
+
+        use_recurrent = (
+            past_key_value is not None
+            and past_key_value[2] > 0
+            and seq_len == 1
+        )
+
+        # Projections (shared)
+        mixed_qkv = self.in_proj_qkv(x).transpose(1, 2)  # [B, conv_dim, seq_len]
+        z = self.in_proj_z(x)
+        b = self.in_proj_b(x)
+        a = self.in_proj_a(x)
+
+        # Conv1d
+        if use_recurrent:
+            recurrent_state, conv_state, step_index = past_key_value
+            conv_weight = comfy.model_management.cast_to_device(self.conv1d.weight, mixed_qkv.device, mixed_qkv.dtype).squeeze(1)
+            conv_bias = comfy.model_management.cast_to_device(self.conv1d.bias, mixed_qkv.device, mixed_qkv.dtype) if self.conv1d.bias is not None else None
+            mixed_qkv = torch_causal_conv1d_update(mixed_qkv, conv_state, conv_weight, conv_bias)
+        else:
+            if past_key_value is not None:
+                recurrent_state, conv_state, step_index = past_key_value
+                conv_state_init = F.pad(mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0))
+                conv_state.copy_(conv_state_init[:, :, -conv_state.shape[-1]:])
+            mixed_qkv = F.silu(self.conv1d(mixed_qkv)[:, :, :seq_len])
+
+        # Split QKV and compute beta/g
+        mixed_qkv = mixed_qkv.transpose(1, 2)  # [B, seq_len, conv_dim]
+        query, key, value = mixed_qkv.split([self.key_dim, self.key_dim, self.value_dim], dim=-1)
+        beta = b.sigmoid()
+        g = -self.A_log.float().exp() * F.softplus(a.float() + self.dt_bias.float())
+
+        # Delta rule
+        if use_recurrent:
+            # single-token path: work in [B, heads, dim] without seq dim
+            query = query.reshape(batch_size, self.num_key_heads, self.key_head_dim)
+            key = key.reshape(batch_size, self.num_key_heads, self.key_head_dim)
+            value = value.reshape(batch_size, self.num_value_heads, self.value_head_dim)
+
+            if self.num_value_heads != self.num_key_heads:
+                rep = self.num_value_heads // self.num_key_heads
+                query = query.repeat_interleave(rep, dim=1)
+                key = key.repeat_interleave(rep, dim=1)
+
+            scale = self.key_head_dim ** -0.5
+            q = F.normalize(query.float(), dim=-1) * scale
+            k = F.normalize(key.float(), dim=-1)
+            v = value.float()
+            beta_t = beta.reshape(batch_size, -1)
+            g_t = g.reshape(batch_size, -1).exp()
+
+            # In-place state update: [B, heads, k_dim, v_dim]
+            recurrent_state.mul_(g_t[:, :, None, None])
+            kv_mem = torch.einsum('bhk,bhkv->bhv', k, recurrent_state)
+            delta = (v - kv_mem) * beta_t[:, :, None]
+            recurrent_state.add_(k.unsqueeze(-1) * delta.unsqueeze(-2))
+            core_attn_out = torch.einsum('bhk,bhkv->bhv', q, recurrent_state)
+
+            core_attn_out = core_attn_out.to(x.dtype).unsqueeze(1)
+            present_key_value = (recurrent_state, conv_state, step_index + 1)
+        else:
+            query = query.reshape(batch_size, seq_len, -1, self.key_head_dim)
+            key = key.reshape(batch_size, seq_len, -1, self.key_head_dim)
+            value = value.reshape(batch_size, seq_len, -1, self.value_head_dim)
+
+            if self.num_value_heads != self.num_key_heads:
+                rep = self.num_value_heads // self.num_key_heads
+                query = query.repeat_interleave(rep, dim=2)
+                key = key.repeat_interleave(rep, dim=2)
+
+            core_attn_out, last_recurrent_state = torch_chunk_gated_delta_rule(
+                query, key, value, g=g, beta=beta,
+                initial_state=None,
+                output_final_state=past_key_value is not None,
+            )
+
+            present_key_value = None
+            if past_key_value is not None:
+                if last_recurrent_state is not None:
+                    recurrent_state.copy_(last_recurrent_state.to(recurrent_state.dtype))
+                present_key_value = (recurrent_state, conv_state, step_index + seq_len)
+
+        # Gated norm + output projection (shared)
+        core_attn_out = self.norm(core_attn_out.reshape(-1, self.value_head_dim), z.reshape(-1, self.value_head_dim))
+        output = self.out_proj(core_attn_out.reshape(batch_size, seq_len, -1))
+        return output, present_key_value
+
+
+# GatedAttention - Full Attention with output gating
+def precompute_partial_rope(head_dim, rotary_dim, position_ids, theta, device=None, mrope_section=None):
+    """Compute RoPE frequencies for partial rotary embeddings."""
+    theta_numerator = torch.arange(0, rotary_dim, 2, device=device).float()
+    inv_freq = 1.0 / (theta ** (theta_numerator / rotary_dim))
+
+    inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+    position_ids_expanded = position_ids[:, None, :].float()
+    freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+    emb = torch.cat((freqs, freqs), dim=-1)
+    cos = emb.cos()
+    sin = emb.sin()
+
+    if mrope_section is not None and position_ids.shape[0] == 3:
+        mrope_section_2 = [s * 2 for s in mrope_section]
+        cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section_2, dim=-1))], dim=-1).unsqueeze(0)
+        sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section_2, dim=-1))], dim=-1).unsqueeze(0)
+
+    cos = cos.unsqueeze(1)
+    sin = sin.unsqueeze(1)
+    sin_split = sin.shape[-1] // 2
+    return (cos, sin[..., :sin_split], -sin[..., sin_split:])
+
+
+def apply_partial_rope(xq, xk, freqs_cis, rotary_dim):
+    """Apply RoPE to only the first rotary_dim dimensions."""
+    xq_rot = xq[..., :rotary_dim]
+    xq_pass = xq[..., rotary_dim:]
+    xk_rot = xk[..., :rotary_dim]
+    xk_pass = xk[..., rotary_dim:]
+
+    xq_rot, xk_rot = apply_rope(xq_rot, xk_rot, freqs_cis)
+
+    xq = torch.cat([xq_rot, xq_pass], dim=-1)
+    xk = torch.cat([xk_rot, xk_pass], dim=-1)
+    return xq, xk
+
+
+class GatedAttention(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+
+        self.num_heads = config.num_attention_heads
+        self.num_kv_heads = config.num_key_value_heads
+        self.head_dim = config.head_dim
+        self.hidden_size = config.hidden_size
+        self.inner_size = self.num_heads * self.head_dim
+        self.rotary_dim = int(self.head_dim * config.partial_rotary_factor)
+
+        # q_proj outputs 2x: query + gate
+        self.q_proj = ops.Linear(config.hidden_size, self.inner_size * 2, bias=config.qkv_bias, device=device, dtype=dtype)
+        self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
+        self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
+        self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype)
+
+        # QK norms with (1+weight) scaling
+        self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+
+    def forward(self, x, attention_mask=None, freqs_cis=None, optimized_attention=None, past_key_value=None):
+        batch_size, seq_length, _ = x.shape
+
+        # Project Q (with gate), K, V
+        qg = self.q_proj(x)
+        # Split into query and gate: each is [B, seq, inner_size]
+        qg = qg.view(batch_size, seq_length, self.num_heads, self.head_dim * 2)
+        xq, gate = qg[..., :self.head_dim], qg[..., self.head_dim:]
+        gate = gate.reshape(batch_size, seq_length, -1)  # [B, seq, inner_size]
+
+        xk = self.k_proj(x)
+        xv = self.v_proj(x)
+
+        xq = self.q_norm(xq).transpose(1, 2)  # [B, heads, seq, head_dim]
+        xk = self.k_norm(xk.view(batch_size, seq_length, self.num_kv_heads, self.head_dim)).transpose(1, 2)
+        xv = xv.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2)
+
+        # Apply partial RoPE
+        xq, xk = apply_partial_rope(xq, xk, freqs_cis, self.rotary_dim)
+
+        # KV cache
+        present_key_value = None
+        if past_key_value is not None:
+            past_key, past_value, index = past_key_value
+            num_tokens = xk.shape[2]
+            if past_key.shape[2] >= (index + num_tokens):
+                past_key[:, :, index:index + num_tokens] = xk
+                past_value[:, :, index:index + num_tokens] = xv
+                xk = past_key[:, :, :index + num_tokens]
+                xv = past_value[:, :, :index + num_tokens]
+                present_key_value = (past_key, past_value, index + num_tokens)
+            else:
+                if index > 0:
+                    xk = torch.cat((past_key[:, :, :index], xk), dim=2)
+                    xv = torch.cat((past_value[:, :, :index], xv), dim=2)
+                present_key_value = (xk, xv, index + num_tokens)
+
+        # Expand KV heads for GQA
+        if self.num_heads != self.num_kv_heads:
+            xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
+            xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
+
+        output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True)
+        output = output * gate.sigmoid()
+
+        return self.o_proj(output), present_key_value
+
+
+# Hybrid Transformer Block
+class Qwen35TransformerBlock(nn.Module):
+    def __init__(self, config, index, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.layer_type = config.layer_types[index]
+        if self.layer_type == "linear_attention":
+            self.linear_attn = GatedDeltaNet(config, device=device, dtype=dtype, ops=ops)
+        else:
+            self.self_attn = GatedAttention(config, device=device, dtype=dtype, ops=ops)
+        self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+
+    def forward(self, x, attention_mask=None, freqs_cis=None, optimized_attention=None, past_key_value=None):
+        if self.layer_type == "linear_attention":
+            h, present_key_value = self.linear_attn(self.input_layernorm(x), attention_mask=attention_mask, past_key_value=past_key_value)
+        else:
+            h, present_key_value = self.self_attn(self.input_layernorm(x), attention_mask=attention_mask, freqs_cis=freqs_cis, optimized_attention=optimized_attention, past_key_value=past_key_value)
+
+        x = x + h
+        x = x + self.mlp(self.post_attention_layernorm(x))
+        return x, present_key_value
+
+
+# Qwen35 Transformer Backbone
+class Qwen35Transformer(Llama2_):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        nn.Module.__init__(self)
+        self.config = config
+        self.vocab_size = config.vocab_size
+        self.normalize_in = False
+
+        self.embed_tokens = ops.Embedding(config.vocab_size, config.hidden_size, device=device, dtype=dtype)
+        self.layers = nn.ModuleList([
+            Qwen35TransformerBlock(config, index=i, device=device, dtype=dtype, ops=ops)
+            for i in range(config.num_hidden_layers)
+        ])
+
+        if config.final_norm:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        else:
+            self.norm = None
+
+        if config.lm_head:
+            self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
+
+    def get_past_len(self, past_key_values):
+        for i, layer in enumerate(self.layers):
+            if layer.layer_type == "full_attention":
+                if len(past_key_values) > i:
+                    return past_key_values[i][2]
+                break
+        return 0
+
+    def compute_freqs_cis(self, position_ids, device):
+        rotary_dim = int(self.config.head_dim * self.config.partial_rotary_factor)
+        return precompute_partial_rope(
+            self.config.head_dim, rotary_dim, position_ids,
+            self.config.rope_theta, device=device,
+            mrope_section=self.config.mrope_section,
+        )
+
+
+# Vision Encoder
+class Qwen35VisionPatchEmbed(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.patch_size = config["patch_size"]
+        self.temporal_patch_size = config["temporal_patch_size"]
+        self.in_channels = config["in_channels"]
+        self.embed_dim = config["hidden_size"]
+        kernel_size = [self.temporal_patch_size, self.patch_size, self.patch_size]
+        self.proj = ops.Conv3d(self.in_channels, self.embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=True, device=device, dtype=dtype)
+
+    def forward(self, x):
+        target_dtype = self.proj.weight.dtype
+        x = x.view(-1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size)
+        return self.proj(x.to(target_dtype)).view(-1, self.embed_dim)
+
+
+class Qwen35VisionMLP(nn.Module):
+    def __init__(self, hidden_size, intermediate_size, device=None, dtype=None, ops=None):
+        super().__init__()
+
+        self.linear_fc1 = ops.Linear(hidden_size, intermediate_size, bias=True, device=device, dtype=dtype)
+        self.linear_fc2 = ops.Linear(intermediate_size, hidden_size, bias=True, device=device, dtype=dtype)
+
+    def forward(self, hidden_state):
+        return self.linear_fc2(F.gelu(self.linear_fc1(hidden_state), approximate="tanh"))
+
+
+class Qwen35VisionRotaryEmbedding(nn.Module):
+    def __init__(self, dim, theta=10000.0):
+        super().__init__()
+        self.dim = dim
+        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    def forward(self, seqlen):
+        seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+        freqs = torch.outer(seq, self.inv_freq)
+        return freqs
+
+
+class Qwen35VisionAttention(nn.Module):
+    def __init__(self, hidden_size, num_heads, device=None, dtype=None, ops=None):
+        super().__init__()
+
+        self.dim = hidden_size
+        self.num_heads = num_heads
+        self.head_dim = self.dim // self.num_heads
+        self.qkv = ops.Linear(self.dim, self.dim * 3, bias=True, device=device, dtype=dtype)
+        self.proj = ops.Linear(self.dim, self.dim, device=device, dtype=dtype)
+
+    def forward(self, x, cu_seqlens, position_embeddings, optimized_attention=None):
+        seq_length = x.shape[0]
+        query_states, key_states, value_states = (
+            self.qkv(x).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
+        )
+        query_states, key_states = apply_rope(query_states, key_states, position_embeddings)
+
+        # Process per-sequence attention
+        lengths = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
+        q_splits = torch.split(query_states, lengths, dim=0)
+        k_splits = torch.split(key_states, lengths, dim=0)
+        v_splits = torch.split(value_states, lengths, dim=0)
+
+        attn_outputs = []
+        for q, k, v in zip(q_splits, k_splits, v_splits):
+            q = q.transpose(0, 1).unsqueeze(0)
+            k = k.transpose(0, 1).unsqueeze(0)
+            v = v.transpose(0, 1).unsqueeze(0)
+            attn_outputs.append(optimized_attention(q, k, v, self.num_heads, skip_reshape=True))
+
+        attn_output = torch.cat(attn_outputs, dim=1)
+        attn_output = attn_output.reshape(seq_length, -1)
+        return self.proj(attn_output)
+
+
+class Qwen35VisionBlock(nn.Module):
+    def __init__(self, hidden_size, num_heads, intermediate_size, device=None, dtype=None, ops=None):
+        super().__init__()
+
+        self.norm1 = ops.LayerNorm(hidden_size, eps=1e-6, device=device, dtype=dtype)
+        self.norm2 = ops.LayerNorm(hidden_size, eps=1e-6, device=device, dtype=dtype)
+        self.attn = Qwen35VisionAttention(hidden_size, num_heads, device=device, dtype=dtype, ops=ops)
+        self.mlp = Qwen35VisionMLP(hidden_size, intermediate_size, device=device, dtype=dtype, ops=ops)
+
+    def forward(self, x, cu_seqlens, position_embeddings, optimized_attention=None):
+        x = x + self.attn(self.norm1(x), cu_seqlens=cu_seqlens, position_embeddings=position_embeddings, optimized_attention=optimized_attention)
+        return x + self.mlp(self.norm2(x))
+
+
+class Qwen35VisionPatchMerger(nn.Module):
+    def __init__(self, hidden_size, spatial_merge_size, out_hidden_size, device=None, dtype=None, ops=None):
+        super().__init__()
+
+        merge_dim = hidden_size * (spatial_merge_size ** 2)
+        self.norm = ops.LayerNorm(hidden_size, eps=1e-6, device=device, dtype=dtype)
+        self.linear_fc1 = ops.Linear(merge_dim, merge_dim, device=device, dtype=dtype)
+        self.linear_fc2 = ops.Linear(merge_dim, out_hidden_size, device=device, dtype=dtype)
+        self.merge_dim = merge_dim
+
+    def forward(self, x):
+        x = self.norm(x).view(-1, self.merge_dim)
+        return self.linear_fc2(F.gelu(self.linear_fc1(x)))
+
+
+class Qwen35VisionModel(nn.Module):
+    def __init__(self, config, device=None, dtype=None, ops=None):
+        super().__init__()
+        self.spatial_merge_size = config["spatial_merge_size"]
+        self.patch_size = config["patch_size"]
+        self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size
+
+        self.hidden_size = config["hidden_size"]
+        self.num_heads = config["num_heads"]
+        self.num_position_embeddings = config["num_position_embeddings"]
+
+        self.patch_embed = Qwen35VisionPatchEmbed(config, device=device, dtype=dtype, ops=ops)
+        self.pos_embed = ops.Embedding(self.num_position_embeddings, self.hidden_size, device=device, dtype=dtype)
+        self.num_grid_per_side = int(self.num_position_embeddings ** 0.5)
+        self.rotary_pos_emb = Qwen35VisionRotaryEmbedding(self.hidden_size // self.num_heads // 2)
+        self.blocks = nn.ModuleList([
+            Qwen35VisionBlock(self.hidden_size, self.num_heads, config["intermediate_size"], device=device, dtype=dtype, ops=ops)
+            for _ in range(config["depth"])
+        ])
+        self.merger = Qwen35VisionPatchMerger(self.hidden_size, self.spatial_merge_size, config["out_hidden_size"], device=device, dtype=dtype, ops=ops)
+
+    def rot_pos_emb(self, grid_thw):
+        merge_size = self.spatial_merge_size
+        grid_thw_list = grid_thw.tolist()
+        max_hw = max(max(h, w) for _, h, w in grid_thw_list)
+        freq_table = self.rotary_pos_emb(max_hw)
+        device = freq_table.device
+        total_tokens = sum(int(t * h * w) for t, h, w in grid_thw_list)
+        pos_ids = torch.empty((total_tokens, 2), dtype=torch.long, device=device)
+        offset = 0
+        for num_frames, height, width in grid_thw_list:
+            num_frames, height, width = int(num_frames), int(height), int(width)
+            merged_h, merged_w = height // merge_size, width // merge_size
+            block_rows = torch.arange(merged_h, device=device)
+            block_cols = torch.arange(merged_w, device=device)
+            intra_row = torch.arange(merge_size, device=device)
+            intra_col = torch.arange(merge_size, device=device)
+            row_idx = block_rows[:, None, None, None] * merge_size + intra_row[None, None, :, None]
+            col_idx = block_cols[None, :, None, None] * merge_size + intra_col[None, None, None, :]
+            row_idx = row_idx.expand(merged_h, merged_w, merge_size, merge_size).reshape(-1)
+            col_idx = col_idx.expand(merged_h, merged_w, merge_size, merge_size).reshape(-1)
+            coords = torch.stack((row_idx, col_idx), dim=-1)
+            if num_frames > 1:
+                coords = coords.repeat(num_frames, 1)
+            num_tokens = coords.shape[0]
+            pos_ids[offset:offset + num_tokens] = coords
+            offset += num_tokens
+        embeddings = freq_table[pos_ids]
+        embeddings = embeddings.flatten(1)
+        return embeddings
+
+    def fast_pos_embed_interpolate(self, grid_thw):
+        grid_thw_list = grid_thw.tolist()
+        grid_ts = [int(row[0]) for row in grid_thw_list]
+        grid_hs = [int(row[1]) for row in grid_thw_list]
+        grid_ws = [int(row[2]) for row in grid_thw_list]
+        device = self.pos_embed.weight.device
+        idx_list = [[] for _ in range(4)]
+        weight_list = [[] for _ in range(4)]
+        for t, h, w in grid_thw_list:
+            h, w = int(h), int(w)
+            h_idxs = torch.linspace(0, self.num_grid_per_side - 1, h, device=device)
+            w_idxs = torch.linspace(0, self.num_grid_per_side - 1, w, device=device)
+            h_idxs_floor = h_idxs.int()
+            w_idxs_floor = w_idxs.int()
+            h_idxs_ceil = (h_idxs.int() + 1).clip(max=self.num_grid_per_side - 1)
+            w_idxs_ceil = (w_idxs.int() + 1).clip(max=self.num_grid_per_side - 1)
+            dh = h_idxs - h_idxs_floor
+            dw = w_idxs - w_idxs_floor
+            base_h = h_idxs_floor * self.num_grid_per_side
+            base_h_ceil = h_idxs_ceil * self.num_grid_per_side
+            indices = [
+                (base_h[None].T + w_idxs_floor[None]).flatten(),
+                (base_h[None].T + w_idxs_ceil[None]).flatten(),
+                (base_h_ceil[None].T + w_idxs_floor[None]).flatten(),
+                (base_h_ceil[None].T + w_idxs_ceil[None]).flatten(),
+            ]
+            weights = [
+                ((1 - dh)[None].T * (1 - dw)[None]).flatten(),
+                ((1 - dh)[None].T * dw[None]).flatten(),
+                (dh[None].T * (1 - dw)[None]).flatten(),
+                (dh[None].T * dw[None]).flatten(),
+            ]
+            for j in range(4):
+                idx_list[j].extend(indices[j].tolist())
+                weight_list[j].extend(weights[j].tolist())
+        idx_tensor = torch.tensor(idx_list, dtype=torch.long, device=device)
+        weight_tensor = torch.tensor(weight_list, dtype=self.pos_embed.weight.dtype, device=device)
+        pos_embeds = self.pos_embed(idx_tensor).to(device) * weight_tensor[:, :, None]
+        patch_pos_embeds = pos_embeds[0] + pos_embeds[1] + pos_embeds[2] + pos_embeds[3]
+        patch_pos_embeds = patch_pos_embeds.split([h * w for h, w in zip(grid_hs, grid_ws)])
+        patch_pos_embeds_permute = []
+        merge_size = self.spatial_merge_size
+        for pos_embed, t, h, w in zip(patch_pos_embeds, grid_ts, grid_hs, grid_ws):
+            pos_embed = pos_embed.repeat(t, 1)
+            pos_embed = (
+                pos_embed.view(t, h // merge_size, merge_size, w // merge_size, merge_size, -1)
+                .permute(0, 1, 3, 2, 4, 5)
+                .flatten(0, 4)
+            )
+            patch_pos_embeds_permute.append(pos_embed)
+        return torch.cat(patch_pos_embeds_permute)
+
+    def forward(self, x, grid_thw):
+        x = self.patch_embed(x)
+        pos_embeds = self.fast_pos_embed_interpolate(grid_thw).to(x.device)
+        x = x + pos_embeds
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+        seq_len = x.shape[0]
+        x = x.reshape(seq_len, -1)
+        rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
+        emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+        cos = emb.cos().unsqueeze(-2)
+        sin = emb.sin().unsqueeze(-2)
+        sin_half = sin.shape[-1] // 2
+        position_embeddings = (cos, sin[..., :sin_half], -sin[..., sin_half:])
+        cu_seqlens = torch.repeat_interleave(
+            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
+        ).cumsum(dim=0, dtype=torch.int32)
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+        optimized_attention = optimized_attention_for_device(x.device, mask=False, small_input=True)
+        for blk in self.blocks:
+            x = blk(x, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings, optimized_attention=optimized_attention)
+        merged = self.merger(x)
+        return merged
+
+# Model Wrapper
+class Qwen35(BaseLlama, BaseGenerate, torch.nn.Module):
+    model_type = "qwen35_2b"
+
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = _make_config(self.model_type, config_dict)
+        self.num_layers = config.num_hidden_layers
+        self.model = Qwen35Transformer(config, device=device, dtype=dtype, ops=operations)
+        vision_overrides = QWEN35_MODELS.get(self.model_type, {}).get("vision", {})
+        vision_config = {**QWEN35_VISION_DEFAULTS, **vision_overrides, "out_hidden_size": config.hidden_size}
+        self.visual = Qwen35VisionModel(vision_config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
+
+    def preprocess_embed(self, embed, device):
+        if embed["type"] == "image":
+            image, grid = comfy.text_encoders.qwen_vl.process_qwen2vl_images(embed["data"], patch_size=16)
+            return self.visual(image.to(device, dtype=torch.float32), grid), grid
+        return None, None
+
+    def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[], past_key_values=None):
+        grid = None
+        position_ids = None
+        offset = 0
+        for e in embeds_info:
+            if e.get("type") == "image":
+                grid = e.get("extra", None)
+                start = e.get("index")
+                if position_ids is None:
+                    position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device)
+                    position_ids[:, :start] = torch.arange(0, start, device=embeds.device)
+                end = e.get("size") + start
+                len_max = int(grid.max()) // 2
+                start_next = len_max + start
+                position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device)
+                position_ids[0, start:end] = start + offset
+                max_d = int(grid[0][1]) // 2
+                position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]
+                max_d = int(grid[0][2]) // 2
+                position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start]
+                offset += len_max - (end - start)
+
+        if grid is None:
+            position_ids = None
+
+        return super().forward(x, attention_mask=attention_mask, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=final_layer_norm_intermediate, dtype=dtype, position_ids=position_ids, past_key_values=past_key_values)
+
+    def init_kv_cache(self, batch, max_cache_len, device, execution_dtype):
+        model_config = self.model.config
+        past_key_values = []
+        for i in range(model_config.num_hidden_layers):
+            if model_config.layer_types[i] == "linear_attention":
+                recurrent_state = torch.zeros(
+                    [batch, model_config.linear_num_value_heads, model_config.linear_key_head_dim, model_config.linear_value_head_dim],
+                    device=device, dtype=torch.float32
+                )
+                conv_dim = model_config.linear_num_key_heads * model_config.linear_key_head_dim * 2 + model_config.linear_num_value_heads * model_config.linear_value_head_dim
+                conv_state = torch.zeros(
+                    [batch, conv_dim, model_config.conv_kernel_size - 1],
+                    device=device, dtype=execution_dtype
+                )
+                past_key_values.append((recurrent_state, conv_state, 0))
+            else:
+                past_key_values.append((
+                    torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),
+                    torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype),
+                    0
+                ))
+        return past_key_values
+
+# Tokenizer and Text Encoder Wrappers
+
+class Qwen35Tokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}, embedding_size=2048, embedding_key="qwen35_2b"):
+        from transformers import Qwen2Tokenizer
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen35_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=embedding_size, embedding_key=embedding_key, tokenizer_class=Qwen2Tokenizer,
+            has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=248044, tokenizer_data=tokenizer_data)
+
+
+class Qwen35ImageTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}, model_type="qwen35_2b"):
+        embedding_size = QWEN35_MODELS.get(model_type, {}).get("hidden_size", 2048)
+        tokenizer = lambda *a, **kw: Qwen35Tokenizer(*a, **kw, embedding_size=embedding_size, embedding_key=model_type)
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=model_type, tokenizer=tokenizer)
+        self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+        self.llama_template_images = "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=False, **kwargs):
+        image = kwargs.get("image", None)
+        if image is not None and len(images) == 0:
+            images = [image]
+
+        skip_template = False
+        if text.startswith('<|im_start|>'):
+            skip_template = True
+        if prevent_empty_text and text == '':
+            text = ' '
+
+        if skip_template:
+            llama_text = text
+        else:
+            if llama_template is None:
+                if len(images) > 0:
+                    llama_text = self.llama_template_images.format(text)
+                else:
+                    llama_text = self.llama_template.format(text)
+            else:
+                llama_text = llama_template.format(text)
+            if not thinking:
+                llama_text += "<think>\n</think>\n"
+
+        tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
+        key_name = next(iter(tokens))
+        embed_count = 0
+        qwen_tokens = tokens[key_name]
+        for r in qwen_tokens:
+            for i in range(len(r)):
+                if r[i][0] == 248056:  # <|image_pad|>
+                    if len(images) > embed_count:
+                        r[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"},) + r[i][1:]
+                        embed_count += 1
+        return tokens
+
+
+class Qwen35ClipModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}, model_type="qwen35_2b"):
+        class Qwen35_(Qwen35):
+            pass
+        Qwen35_.model_type = model_type
+
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={},
+            dtype=dtype, special_tokens={"pad": 248044}, layer_norm_hidden_state=False,
+            model_class=Qwen35_, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+class Qwen35TEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}, model_type="qwen35_2b"):
+        clip_model = lambda **kw: Qwen35ClipModel(**kw, model_type=model_type)
+        super().__init__(device=device, dtype=dtype, name=model_type, clip_model=clip_model, model_options=model_options)
+
+
+def tokenizer(model_type="qwen35_2b"):
+    class Qwen35ImageTokenizer_(Qwen35ImageTokenizer):
+        def __init__(self, embedding_directory=None, tokenizer_data={}):
+            super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type=model_type)
+    return Qwen35ImageTokenizer_
+
+
+def te(dtype_llama=None, llama_quantization_metadata=None, model_type="qwen35_2b"):
+    class Qwen35TEModel_(Qwen35TEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            super().__init__(device=device, dtype=dtype, model_options=model_options, model_type=model_type)
+    return Qwen35TEModel_

comfy_extras/nodes_textgen.py

@@ -15,6 +15,7 @@ class TextGenerate(io.ComfyNode):
                     io.Float.Input("min_p", default=0.05, min=0.0, max=1.0, step=0.01),
                     io.Float.Input("repetition_penalty", default=1.05, min=0.0, max=5.0, step=0.01),
                     io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff),
+                    io.Float.Input("presence_penalty", optional=True, default=0.0, min=0.0, max=5.0, step=0.01),
                 ]
             ),
             io.DynamicCombo.Option(
@@ -25,7 +26,7 @@ class TextGenerate(io.ComfyNode):
 
         return io.Schema(
             node_id="TextGenerate",
-            category="textgen/",
+            category="textgen",
             search_aliases=["LLM", "gemma"],
             inputs=[
                 io.Clip.Input("clip"),
@@ -33,6 +34,7 @@ class TextGenerate(io.ComfyNode):
                 io.Image.Input("image", optional=True),
                 io.Int.Input("max_length", default=256, min=1, max=2048),
                 io.DynamicCombo.Input("sampling_mode", options=sampling_options, display_name="Sampling Mode"),
+                io.Boolean.Input("thinking", optional=True, default=False, tooltip="Operate in thinking mode if the model supports it."),
             ],
             outputs=[
                 io.String.Output(display_name="generated_text"),
@@ -40,9 +42,9 @@ class TextGenerate(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, clip, prompt, max_length, sampling_mode, image=None) -> io.NodeOutput:
+    def execute(cls, clip, prompt, max_length, sampling_mode, image=None, thinking=False) -> io.NodeOutput:
 
-        tokens = clip.tokenize(prompt, image=image, skip_template=False, min_length=1)
+        tokens = clip.tokenize(prompt, image=image, skip_template=False, min_length=1, thinking=thinking)
 
         # Get sampling parameters from dynamic combo
         do_sample = sampling_mode.get("sampling_mode") == "on"
@@ -52,6 +54,7 @@ class TextGenerate(io.ComfyNode):
         min_p = sampling_mode.get("min_p", 0.0)
         seed = sampling_mode.get("seed", None)
         repetition_penalty = sampling_mode.get("repetition_penalty", 1.0)
+        presence_penalty = sampling_mode.get("presence_penalty", 0.0)
 
         generated_ids = clip.generate(
             tokens,
@@ -62,6 +65,7 @@ class TextGenerate(io.ComfyNode):
             top_p=top_p,
             min_p=min_p,
             repetition_penalty=repetition_penalty,
+            presence_penalty=presence_penalty,
             seed=seed
         )
 
@@ -156,12 +160,12 @@ class TextGenerateLTX2Prompt(TextGenerate):
         )
 
     @classmethod
-    def execute(cls, clip, prompt, max_length, sampling_mode, image=None) -> io.NodeOutput:
+    def execute(cls, clip, prompt, max_length, sampling_mode, image=None, thinking=False) -> io.NodeOutput:
         if image is None:
             formatted_prompt = f"<start_of_turn>system\n{LTX2_T2V_SYSTEM_PROMPT.strip()}<end_of_turn>\n<start_of_turn>user\nUser Raw Input Prompt: {prompt}.<end_of_turn>\n<start_of_turn>model\n"
         else:
             formatted_prompt = f"<start_of_turn>system\n{LTX2_I2V_SYSTEM_PROMPT.strip()}<end_of_turn>\n<start_of_turn>user\n\n<image_soft_token>\n\nUser Raw Input Prompt: {prompt}.<end_of_turn>\n<start_of_turn>model\n"
-        return super().execute(clip, formatted_prompt, max_length, sampling_mode, image)
+        return super().execute(clip, formatted_prompt, max_length, sampling_mode, image, thinking)
 
 
 class TextgenExtension(ComfyExtension):