support step1x

diffsynth/models/qwenvl.py

@@ -0,0 +1,168 @@
+import torch
+
+
+class Qwen25VL_7b_Embedder(torch.nn.Module):
+    def __init__(self, model_path, max_length=640, dtype=torch.bfloat16, device="cuda"):
+        super(Qwen25VL_7b_Embedder, self).__init__()
+        self.max_length = max_length
+        self.dtype = dtype
+        self.device = device
+        
+        from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+
+        self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+            model_path,
+            torch_dtype=dtype,
+        ).to(torch.cuda.current_device())
+
+        self.model.requires_grad_(False)
+        self.processor = AutoProcessor.from_pretrained(
+            model_path, min_pixels=256 * 28 * 28, max_pixels=324 * 28 * 28
+        )
+        
+        Qwen25VL_7b_PREFIX = '''Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:
+- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.
+- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.\n
+Here are examples of how to transform or refine prompts:
+- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.
+- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.\n
+Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:
+User Prompt:'''
+
+        self.prefix = Qwen25VL_7b_PREFIX
+        
+    @staticmethod
+    def from_pretrained(path, torch_dtype=torch.bfloat16, device="cuda"):
+        return Qwen25VL_7b_Embedder(path, dtype=torch_dtype, device=device)
+
+    def forward(self, caption, ref_images):
+        text_list = caption
+        embs = torch.zeros(
+            len(text_list),
+            self.max_length,
+            self.model.config.hidden_size,
+            dtype=torch.bfloat16,
+            device=torch.cuda.current_device(),
+        )
+        hidden_states = torch.zeros(
+            len(text_list),
+            self.max_length,
+            self.model.config.hidden_size,
+            dtype=torch.bfloat16,
+            device=torch.cuda.current_device(),
+        )
+        masks = torch.zeros(
+            len(text_list),
+            self.max_length,
+            dtype=torch.long,
+            device=torch.cuda.current_device(),
+        )
+        input_ids_list = []
+        attention_mask_list = []
+        emb_list = []
+
+        def split_string(s):
+            s = s.replace("“", '"').replace("”", '"').replace("'", '''"''')  # use english quotes
+            result = []
+            in_quotes = False
+            temp = ""
+
+            for idx,char in enumerate(s):
+                if char == '"' and idx>155:
+                    temp += char
+                    if not in_quotes:
+                        result.append(temp)
+                        temp = ""
+
+                    in_quotes = not in_quotes
+                    continue
+                if in_quotes:
+                    if char.isspace():
+                        pass  # have space token
+
+                    result.append("“" + char + "”")
+                else:
+                    temp += char
+
+            if temp:
+                result.append(temp)
+
+            return result
+
+        for idx, (txt, imgs) in enumerate(zip(text_list, ref_images)):
+
+            messages = [{"role": "user", "content": []}]
+
+            messages[0]["content"].append({"type": "text", "text": f"{self.prefix}"})
+
+            messages[0]["content"].append({"type": "image", "image": imgs})
+
+            # 再添加 text
+            messages[0]["content"].append({"type": "text", "text": f"{txt}"})
+
+            # Preparation for inference
+            text = self.processor.apply_chat_template(
+                messages, tokenize=False, add_generation_prompt=True, add_vision_id=True
+            )
+
+            image_inputs = [imgs]
+
+            inputs = self.processor(
+                text=[text],
+                images=image_inputs,
+                padding=True,
+                return_tensors="pt",
+            )
+
+            old_inputs_ids = inputs.input_ids
+            text_split_list = split_string(text)
+
+            token_list = []
+            for text_each in text_split_list:
+                txt_inputs = self.processor(
+                    text=text_each,
+                    images=None,
+                    videos=None,
+                    padding=True,
+                    return_tensors="pt",
+                )
+                token_each = txt_inputs.input_ids
+                if token_each[0][0] == 2073 and token_each[0][-1] == 854:
+                    token_each = token_each[:, 1:-1]
+                    token_list.append(token_each)
+                else:
+                    token_list.append(token_each)
+
+            new_txt_ids = torch.cat(token_list, dim=1).to("cuda")
+
+            new_txt_ids = new_txt_ids.to(old_inputs_ids.device)
+
+            idx1 = (old_inputs_ids == 151653).nonzero(as_tuple=True)[1][0]
+            idx2 = (new_txt_ids == 151653).nonzero(as_tuple=True)[1][0]
+            inputs.input_ids = (
+                torch.cat([old_inputs_ids[0, :idx1], new_txt_ids[0, idx2:]], dim=0)
+                .unsqueeze(0)
+                .to("cuda")
+            )
+            inputs.attention_mask = (inputs.input_ids > 0).long().to("cuda")
+            outputs = self.model(
+                input_ids=inputs.input_ids,
+                attention_mask=inputs.attention_mask,
+                pixel_values=inputs.pixel_values.to("cuda"),
+                image_grid_thw=inputs.image_grid_thw.to("cuda"),
+                output_hidden_states=True,
+            )
+
+            emb = outputs["hidden_states"][-1]
+
+            embs[idx, : min(self.max_length, emb.shape[1] - 217)] = emb[0, 217:][
+                : self.max_length
+            ]
+
+            masks[idx, : min(self.max_length, emb.shape[1] - 217)] = torch.ones(
+                (min(self.max_length, emb.shape[1] - 217)),
+                dtype=torch.long,
+                device=torch.cuda.current_device(),
+            )
+
+        return embs, masks

diffsynth/models/step1x_connector.py

@@ -0,0 +1,683 @@
+from typing import Optional
+
+import torch, math
+import torch.nn
+from einops import rearrange
+from torch import nn
+from functools import partial
+from einops import rearrange
+
+
+
+def attention(q, k, v, attn_mask, mode="torch"):
+    q = q.transpose(1, 2)
+    k = k.transpose(1, 2)
+    v = v.transpose(1, 2)
+    x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+    x = rearrange(x, "b n s d -> b s (n d)")
+    return x
+    
+
+
+class MLP(nn.Module):
+    """MLP as used in Vision Transformer, MLP-Mixer and related networks"""
+
+    def __init__(
+        self,
+        in_channels,
+        hidden_channels=None,
+        out_features=None,
+        act_layer=nn.GELU,
+        norm_layer=None,
+        bias=True,
+        drop=0.0,
+        use_conv=False,
+        device=None,
+        dtype=None,
+    ):
+        super().__init__()
+        out_features = out_features or in_channels
+        hidden_channels = hidden_channels or in_channels
+        bias = (bias, bias)
+        drop_probs = (drop, drop)
+        linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
+
+        self.fc1 = linear_layer(
+            in_channels, hidden_channels, bias=bias[0], device=device, dtype=dtype
+        )
+        self.act = act_layer()
+        self.drop1 = nn.Dropout(drop_probs[0])
+        self.norm = (
+            norm_layer(hidden_channels, device=device, dtype=dtype)
+            if norm_layer is not None
+            else nn.Identity()
+        )
+        self.fc2 = linear_layer(
+            hidden_channels, out_features, bias=bias[1], device=device, dtype=dtype
+        )
+        self.drop2 = nn.Dropout(drop_probs[1])
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop1(x)
+        x = self.norm(x)
+        x = self.fc2(x)
+        x = self.drop2(x)
+        return x
+    
+    
+class TextProjection(nn.Module):
+    """
+    Projects text embeddings. Also handles dropout for classifier-free guidance.
+
+    Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
+    """
+
+    def __init__(self, in_channels, hidden_size, act_layer, dtype=None, device=None):
+        factory_kwargs = {"dtype": dtype, "device": device}
+        super().__init__()
+        self.linear_1 = nn.Linear(
+            in_features=in_channels,
+            out_features=hidden_size,
+            bias=True,
+            **factory_kwargs,
+        )
+        self.act_1 = act_layer()
+        self.linear_2 = nn.Linear(
+            in_features=hidden_size,
+            out_features=hidden_size,
+            bias=True,
+            **factory_kwargs,
+        )
+
+    def forward(self, caption):
+        hidden_states = self.linear_1(caption)
+        hidden_states = self.act_1(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+    
+    
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+
+    def __init__(
+        self,
+        hidden_size,
+        act_layer,
+        frequency_embedding_size=256,
+        max_period=10000,
+        out_size=None,
+        dtype=None,
+        device=None,
+    ):
+        factory_kwargs = {"dtype": dtype, "device": device}
+        super().__init__()
+        self.frequency_embedding_size = frequency_embedding_size
+        self.max_period = max_period
+        if out_size is None:
+            out_size = hidden_size
+
+        self.mlp = nn.Sequential(
+            nn.Linear(
+                frequency_embedding_size, hidden_size, bias=True, **factory_kwargs
+            ),
+            act_layer(),
+            nn.Linear(hidden_size, out_size, bias=True, **factory_kwargs),
+        )
+        nn.init.normal_(self.mlp[0].weight, std=0.02)  # type: ignore
+        nn.init.normal_(self.mlp[2].weight, std=0.02)  # type: ignore
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+
+        Args:
+            t (torch.Tensor): a 1-D Tensor of N indices, one per batch element. These may be fractional.
+            dim (int): the dimension of the output.
+            max_period (int): controls the minimum frequency of the embeddings.
+
+        Returns:
+            embedding (torch.Tensor): An (N, D) Tensor of positional embeddings.
+
+        .. ref_link: https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+        """
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period)
+            * torch.arange(start=0, end=half, dtype=torch.float32)
+            / half
+        ).to(device=t.device)
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
+        return embedding
+
+    def forward(self, t):
+        t_freq = self.timestep_embedding(
+            t, self.frequency_embedding_size, self.max_period
+        ).type(self.mlp[0].weight.dtype)  # type: ignore
+        t_emb = self.mlp(t_freq)
+        return t_emb
+    
+    
+def apply_gate(x, gate=None, tanh=False):
+    """AI is creating summary for apply_gate
+
+    Args:
+        x (torch.Tensor): input tensor.
+        gate (torch.Tensor, optional): gate tensor. Defaults to None.
+        tanh (bool, optional): whether to use tanh function. Defaults to False.
+
+    Returns:
+        torch.Tensor: the output tensor after apply gate.
+    """
+    if gate is None:
+        return x
+    if tanh:
+        return x * gate.unsqueeze(1).tanh()
+    else:
+        return x * gate.unsqueeze(1)
+
+
+class RMSNorm(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        elementwise_affine=True,
+        eps: float = 1e-6,
+        device=None,
+        dtype=None,
+    ):
+        """
+        Initialize the RMSNorm normalization layer.
+
+        Args:
+            dim (int): The dimension of the input tensor.
+            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+
+        Attributes:
+            eps (float): A small value added to the denominator for numerical stability.
+            weight (nn.Parameter): Learnable scaling parameter.
+
+        """
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.eps = eps
+        if elementwise_affine:
+            self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs))
+
+    def _norm(self, x):
+        """
+        Apply the RMSNorm normalization to the input tensor.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The normalized tensor.
+
+        """
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        """
+        Forward pass through the RMSNorm layer.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The output tensor after applying RMSNorm.
+
+        """
+        output = self._norm(x.float()).type_as(x)
+        if hasattr(self, "weight"):
+            output = output * self.weight
+        return output
+
+
+def get_norm_layer(norm_layer):
+    """
+    Get the normalization layer.
+
+    Args:
+        norm_layer (str): The type of normalization layer.
+
+    Returns:
+        norm_layer (nn.Module): The normalization layer.
+    """
+    if norm_layer == "layer":
+        return nn.LayerNorm
+    elif norm_layer == "rms":
+        return RMSNorm
+    else:
+        raise NotImplementedError(f"Norm layer {norm_layer} is not implemented")
+
+
+def get_activation_layer(act_type):
+    """get activation layer
+
+    Args:
+        act_type (str): the activation type
+
+    Returns:
+        torch.nn.functional: the activation layer
+    """
+    if act_type == "gelu":
+        return lambda: nn.GELU()
+    elif act_type == "gelu_tanh":
+        return lambda: nn.GELU(approximate="tanh")
+    elif act_type == "relu":
+        return nn.ReLU
+    elif act_type == "silu":
+        return nn.SiLU
+    else:
+        raise ValueError(f"Unknown activation type: {act_type}")
+
+class IndividualTokenRefinerBlock(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_size,
+        heads_num,
+        mlp_width_ratio: str = 4.0,
+        mlp_drop_rate: float = 0.0,
+        act_type: str = "silu",
+        qk_norm: bool = False,
+        qk_norm_type: str = "layer",
+        qkv_bias: bool = True,
+        need_CA: bool = False,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.need_CA = need_CA
+        self.heads_num = heads_num
+        head_dim = hidden_size // heads_num
+        mlp_hidden_dim = int(hidden_size * mlp_width_ratio)
+
+        self.norm1 = nn.LayerNorm(
+            hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
+        )
+        self.self_attn_qkv = nn.Linear(
+            hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs
+        )
+        qk_norm_layer = get_norm_layer(qk_norm_type)
+        self.self_attn_q_norm = (
+            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+            if qk_norm
+            else nn.Identity()
+        )
+        self.self_attn_k_norm = (
+            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+            if qk_norm
+            else nn.Identity()
+        )
+        self.self_attn_proj = nn.Linear(
+            hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs
+        )
+
+        self.norm2 = nn.LayerNorm(
+            hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
+        )
+        act_layer = get_activation_layer(act_type)
+        self.mlp = MLP(
+            in_channels=hidden_size,
+            hidden_channels=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=mlp_drop_rate,
+            **factory_kwargs,
+        )
+
+        self.adaLN_modulation = nn.Sequential(
+            act_layer(),
+            nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs),
+        )
+
+        if self.need_CA:
+            self.cross_attnblock=CrossAttnBlock(hidden_size=hidden_size,
+                        heads_num=heads_num,
+                        mlp_width_ratio=mlp_width_ratio,
+                        mlp_drop_rate=mlp_drop_rate,
+                        act_type=act_type,
+                        qk_norm=qk_norm,
+                        qk_norm_type=qk_norm_type,
+                        qkv_bias=qkv_bias,
+                        **factory_kwargs,)
+        # Zero-initialize the modulation
+        nn.init.zeros_(self.adaLN_modulation[1].weight)
+        nn.init.zeros_(self.adaLN_modulation[1].bias)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        c: torch.Tensor,  # timestep_aware_representations + context_aware_representations
+        attn_mask: torch.Tensor = None,
+        y: torch.Tensor = None,
+    ):
+        gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1)
+
+        norm_x = self.norm1(x)
+        qkv = self.self_attn_qkv(norm_x)
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
+        # Apply QK-Norm if needed
+        q = self.self_attn_q_norm(q).to(v)
+        k = self.self_attn_k_norm(k).to(v)
+
+        # Self-Attention
+        attn = attention(q, k, v, mode="torch", attn_mask=attn_mask)
+
+        x = x + apply_gate(self.self_attn_proj(attn), gate_msa)
+        
+        if self.need_CA:
+            x = self.cross_attnblock(x, c, attn_mask, y)
+
+        # FFN Layer
+        x = x + apply_gate(self.mlp(self.norm2(x)), gate_mlp)
+
+        return x
+
+
+
+
+class CrossAttnBlock(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_size,
+        heads_num,
+        mlp_width_ratio: str = 4.0,
+        mlp_drop_rate: float = 0.0,
+        act_type: str = "silu",
+        qk_norm: bool = False,
+        qk_norm_type: str = "layer",
+        qkv_bias: bool = True,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.heads_num = heads_num
+        head_dim = hidden_size // heads_num
+
+        self.norm1 = nn.LayerNorm(
+            hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
+        )
+        self.norm1_2 = nn.LayerNorm(
+            hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
+        )
+        self.self_attn_q = nn.Linear(
+            hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs
+        )
+        self.self_attn_kv = nn.Linear(
+            hidden_size, hidden_size*2, bias=qkv_bias, **factory_kwargs
+        )
+        qk_norm_layer = get_norm_layer(qk_norm_type)
+        self.self_attn_q_norm = (
+            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+            if qk_norm
+            else nn.Identity()
+        )
+        self.self_attn_k_norm = (
+            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+            if qk_norm
+            else nn.Identity()
+        )
+        self.self_attn_proj = nn.Linear(
+            hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs
+        )
+
+        self.norm2 = nn.LayerNorm(
+            hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
+        )
+        act_layer = get_activation_layer(act_type)
+
+        self.adaLN_modulation = nn.Sequential(
+            act_layer(),
+            nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs),
+        )
+        # Zero-initialize the modulation
+        nn.init.zeros_(self.adaLN_modulation[1].weight)
+        nn.init.zeros_(self.adaLN_modulation[1].bias)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        c: torch.Tensor,  # timestep_aware_representations + context_aware_representations
+        attn_mask: torch.Tensor = None,
+        y: torch.Tensor=None,
+        
+    ):
+        gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1)
+
+        norm_x = self.norm1(x)
+        norm_y = self.norm1_2(y)
+        q = self.self_attn_q(norm_x)
+        q = rearrange(q, "B L (H D) -> B L H D",  H=self.heads_num)
+        kv = self.self_attn_kv(norm_y)
+        k, v = rearrange(kv, "B L (K H D) -> K B L H D", K=2, H=self.heads_num)
+        # Apply QK-Norm if needed
+        q = self.self_attn_q_norm(q).to(v)
+        k = self.self_attn_k_norm(k).to(v)
+
+        # Self-Attention
+        attn = attention(q, k, v, mode="torch", attn_mask=attn_mask)
+
+        x = x + apply_gate(self.self_attn_proj(attn), gate_msa)
+
+        return x
+
+
+
+class IndividualTokenRefiner(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_size,
+        heads_num,
+        depth,
+        mlp_width_ratio: float = 4.0,
+        mlp_drop_rate: float = 0.0,
+        act_type: str = "silu",
+        qk_norm: bool = False,
+        qk_norm_type: str = "layer",
+        qkv_bias: bool = True,
+        need_CA:bool=False,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+    ):  
+        
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.need_CA = need_CA
+        self.blocks = nn.ModuleList(
+            [
+                IndividualTokenRefinerBlock(
+                    hidden_size=hidden_size,
+                    heads_num=heads_num,
+                    mlp_width_ratio=mlp_width_ratio,
+                    mlp_drop_rate=mlp_drop_rate,
+                    act_type=act_type,
+                    qk_norm=qk_norm,
+                    qk_norm_type=qk_norm_type,
+                    qkv_bias=qkv_bias,
+                    need_CA=self.need_CA,
+                    **factory_kwargs,
+                )
+                for _ in range(depth)
+            ]
+        )
+
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        c: torch.LongTensor,
+        mask: Optional[torch.Tensor] = None,
+        y:torch.Tensor=None,
+    ):
+        self_attn_mask = None
+        if mask is not None:
+            batch_size = mask.shape[0]
+            seq_len = mask.shape[1]
+            mask = mask.to(x.device)
+            # batch_size x 1 x seq_len x seq_len
+            self_attn_mask_1 = mask.view(batch_size, 1, 1, seq_len).repeat(
+                1, 1, seq_len, 1
+            )
+            # batch_size x 1 x seq_len x seq_len
+            self_attn_mask_2 = self_attn_mask_1.transpose(2, 3)
+            # batch_size x 1 x seq_len x seq_len, 1 for broadcasting of heads_num
+            self_attn_mask = (self_attn_mask_1 & self_attn_mask_2).bool()
+            # avoids self-attention weight being NaN for padding tokens
+            self_attn_mask[:, :, :, 0] = True
+        
+        
+        for block in self.blocks:
+            x = block(x, c, self_attn_mask,y)
+
+        return x
+
+
+class SingleTokenRefiner(torch.nn.Module):
+    """
+    A single token refiner block for llm text embedding refine.
+    """
+    def __init__(
+        self,
+        in_channels,
+        hidden_size,
+        heads_num,
+        depth,
+        mlp_width_ratio: float = 4.0,
+        mlp_drop_rate: float = 0.0,
+        act_type: str = "silu",
+        qk_norm: bool = False,
+        qk_norm_type: str = "layer",
+        qkv_bias: bool = True,
+        need_CA:bool=False,
+        attn_mode: str = "torch",
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.attn_mode = attn_mode
+        self.need_CA = need_CA
+        assert self.attn_mode == "torch", "Only support 'torch' mode for token refiner."
+
+        self.input_embedder = nn.Linear(
+            in_channels, hidden_size, bias=True, **factory_kwargs
+        )
+        if self.need_CA:
+            self.input_embedder_CA = nn.Linear(
+            in_channels, hidden_size, bias=True, **factory_kwargs
+        )
+
+        act_layer = get_activation_layer(act_type)
+        # Build timestep embedding layer
+        self.t_embedder = TimestepEmbedder(hidden_size, act_layer, **factory_kwargs)
+        # Build context embedding layer
+        self.c_embedder = TextProjection(
+            in_channels, hidden_size, act_layer, **factory_kwargs
+        )
+
+        self.individual_token_refiner = IndividualTokenRefiner(
+            hidden_size=hidden_size,
+            heads_num=heads_num,
+            depth=depth,
+            mlp_width_ratio=mlp_width_ratio,
+            mlp_drop_rate=mlp_drop_rate,
+            act_type=act_type,
+            qk_norm=qk_norm,
+            qk_norm_type=qk_norm_type,
+            qkv_bias=qkv_bias,
+            need_CA=need_CA,
+            **factory_kwargs,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        t: torch.LongTensor,
+        mask: Optional[torch.LongTensor] = None,
+        y: torch.LongTensor=None,
+    ):
+        timestep_aware_representations = self.t_embedder(t)
+
+        if mask is None:
+            context_aware_representations = x.mean(dim=1)
+        else:
+            mask_float = mask.unsqueeze(-1)  # [b, s1, 1]
+            context_aware_representations = (x * mask_float).sum(
+                dim=1
+            ) / mask_float.sum(dim=1)
+        context_aware_representations = self.c_embedder(context_aware_representations)
+        c = timestep_aware_representations + context_aware_representations
+
+        x = self.input_embedder(x)
+        if self.need_CA:
+            y = self.input_embedder_CA(y)
+            x = self.individual_token_refiner(x, c, mask, y)
+        else:
+            x = self.individual_token_refiner(x, c, mask)
+
+        return x
+
+
+class Qwen2Connector(torch.nn.Module):
+    def __init__(
+        self,
+        # biclip_dim=1024,
+        in_channels=3584,
+        hidden_size=4096,
+        heads_num=32,
+        depth=2,
+        need_CA=False,
+        device=None,
+        dtype=torch.bfloat16,
+    ):
+        super().__init__()
+        factory_kwargs = {"device": device, "dtype":dtype}
+
+        self.S =SingleTokenRefiner(in_channels=in_channels,hidden_size=hidden_size,heads_num=heads_num,depth=depth,need_CA=need_CA,**factory_kwargs)
+        self.global_proj_out=nn.Linear(in_channels,768)
+
+        self.scale_factor = nn.Parameter(torch.zeros(1))
+        with torch.no_grad():
+            self.scale_factor.data += -(1 - 0.09)
+
+    def forward(self, x,t,mask):
+        mask_float = mask.unsqueeze(-1)  # [b, s1, 1]
+        x_mean = (x * mask_float).sum(
+                dim=1
+            ) / mask_float.sum(dim=1) * (1 + self.scale_factor)
+
+        global_out=self.global_proj_out(x_mean)
+        encoder_hidden_states = self.S(x,t,mask)
+        return encoder_hidden_states,global_out
+    
+    @staticmethod
+    def state_dict_converter():
+        return Qwen2ConnectorStateDictConverter()
+    
+    
+class Qwen2ConnectorStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+    
+    def from_civitai(self, state_dict):
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name.startswith("connector."):
+                name_ = name[len("connector."):]
+                state_dict_[name_] = param
+        return state_dict_