diff --git "a/diffsynth/models/sd_unet.py" "b/diffsynth/models/sd_unet.py"
new file mode 100644--- /dev/null
+++ "b/diffsynth/models/sd_unet.py"
@@ -0,0 +1,1107 @@
+import torch, math
+from .attention import Attention
+from .tiler import TileWorker
+
+
+class Timesteps(torch.nn.Module):
+    def __init__(self, num_channels):
+        super().__init__()
+        self.num_channels = num_channels
+
+    def forward(self, timesteps):
+        half_dim = self.num_channels // 2
+        exponent = -math.log(10000) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device) / half_dim
+        timesteps = timesteps.unsqueeze(-1)
+        emb = timesteps.float() * torch.exp(exponent)
+        emb = torch.cat([torch.cos(emb), torch.sin(emb)], dim=-1)
+        return emb
+
+
+class GEGLU(torch.nn.Module):
+
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = torch.nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, hidden_states):
+        hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1)
+        return hidden_states * torch.nn.functional.gelu(gate)
+
+
+class BasicTransformerBlock(torch.nn.Module):
+
+    def __init__(self, dim, num_attention_heads, attention_head_dim, cross_attention_dim):
+        super().__init__()
+
+        # 1. Self-Attn
+        self.norm1 = torch.nn.LayerNorm(dim, elementwise_affine=True)
+        self.attn1 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True)
+
+        # 2. Cross-Attn
+        self.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True)
+        self.attn2 = Attention(q_dim=dim, kv_dim=cross_attention_dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True)
+
+        # 3. Feed-forward
+        self.norm3 = torch.nn.LayerNorm(dim, elementwise_affine=True)
+        self.act_fn = GEGLU(dim, dim * 4)
+        self.ff = torch.nn.Linear(dim * 4, dim)
+
+
+    def forward(self, hidden_states, encoder_hidden_states, ipadapter_kwargs=None):
+        # 1. Self-Attention
+        norm_hidden_states = self.norm1(hidden_states)
+        attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None)
+        hidden_states = attn_output + hidden_states
+
+        # 2. Cross-Attention
+        norm_hidden_states = self.norm2(hidden_states)
+        attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states, ipadapter_kwargs=ipadapter_kwargs)
+        hidden_states = attn_output + hidden_states
+
+        # 3. Feed-forward
+        norm_hidden_states = self.norm3(hidden_states)
+        ff_output = self.act_fn(norm_hidden_states)
+        ff_output = self.ff(ff_output)
+        hidden_states = ff_output + hidden_states
+
+        return hidden_states
+
+
+class DownSampler(torch.nn.Module):
+    def __init__(self, channels, padding=1, extra_padding=False):
+        super().__init__()
+        self.conv = torch.nn.Conv2d(channels, channels, 3, stride=2, padding=padding)
+        self.extra_padding = extra_padding
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        if self.extra_padding:
+            hidden_states = torch.nn.functional.pad(hidden_states, (0, 1, 0, 1), mode="constant", value=0)
+        hidden_states = self.conv(hidden_states)
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class UpSampler(torch.nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.conv = torch.nn.Conv2d(channels, channels, 3, padding=1)
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        hidden_states = torch.nn.functional.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
+        hidden_states = self.conv(hidden_states)
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class ResnetBlock(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, temb_channels=None, groups=32, eps=1e-5):
+        super().__init__()
+        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
+        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        if temb_channels is not None:
+            self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)
+        self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
+        self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.nonlinearity = torch.nn.SiLU()
+        self.conv_shortcut = None
+        if in_channels != out_channels:
+            self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True)
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        x = hidden_states
+        x = self.norm1(x)
+        x = self.nonlinearity(x)
+        x = self.conv1(x)
+        if time_emb is not None:
+            emb = self.nonlinearity(time_emb)
+            emb = self.time_emb_proj(emb)[:, :, None, None]
+            x = x + emb
+        x = self.norm2(x)
+        x = self.nonlinearity(x)
+        x = self.conv2(x)
+        if self.conv_shortcut is not None:
+            hidden_states = self.conv_shortcut(hidden_states)
+        hidden_states = hidden_states + x
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class AttentionBlock(torch.nn.Module):
+
+    def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, cross_attention_dim=None, norm_num_groups=32, eps=1e-5, need_proj_out=True):
+        super().__init__()
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True)
+        self.proj_in = torch.nn.Linear(in_channels, inner_dim)
+
+        self.transformer_blocks = torch.nn.ModuleList([
+            BasicTransformerBlock(
+                inner_dim,
+                num_attention_heads,
+                attention_head_dim,
+                cross_attention_dim=cross_attention_dim
+            )
+            for d in range(num_layers)
+        ])
+        self.need_proj_out = need_proj_out
+        if need_proj_out:
+            self.proj_out = torch.nn.Linear(inner_dim, in_channels)
+
+    def forward(
+        self,
+        hidden_states, time_emb, text_emb, res_stack,
+        cross_frame_attention=False,
+        tiled=False, tile_size=64, tile_stride=32,
+        ipadapter_kwargs_list={},
+        **kwargs
+    ):
+        batch, _, height, width = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
+        hidden_states = self.proj_in(hidden_states)
+
+        if cross_frame_attention:
+            hidden_states = hidden_states.reshape(1, batch * height * width, inner_dim)
+            encoder_hidden_states = text_emb.mean(dim=0, keepdim=True)
+        else:
+            encoder_hidden_states = text_emb
+            if encoder_hidden_states.shape[0] != hidden_states.shape[0]:
+                encoder_hidden_states = encoder_hidden_states.repeat(hidden_states.shape[0], 1, 1)
+
+        if tiled:
+            tile_size = min(tile_size, min(height, width))
+            hidden_states = hidden_states.permute(0, 2, 1).reshape(batch, inner_dim, height, width)
+            def block_tile_forward(x):
+                b, c, h, w = x.shape
+                x = x.permute(0, 2, 3, 1).reshape(b, h*w, c)
+                x = block(x, encoder_hidden_states)
+                x = x.reshape(b, h, w, c).permute(0, 3, 1, 2)
+                return x
+            for block in self.transformer_blocks:
+                hidden_states = TileWorker().tiled_forward(
+                    block_tile_forward,
+                    hidden_states,
+                    tile_size,
+                    tile_stride,
+                    tile_device=hidden_states.device,
+                    tile_dtype=hidden_states.dtype
+                )
+            hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
+        else:
+            for block_id, block in enumerate(self.transformer_blocks):
+                hidden_states = block(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    ipadapter_kwargs=ipadapter_kwargs_list.get(block_id, None)
+                )
+        if cross_frame_attention:
+            hidden_states = hidden_states.reshape(batch, height * width, inner_dim)
+
+        if self.need_proj_out:
+            hidden_states = self.proj_out(hidden_states)
+            hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+            hidden_states = hidden_states + residual
+        else:
+            hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class PushBlock(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        res_stack.append(hidden_states)
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class PopBlock(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        res_hidden_states = res_stack.pop()
+        hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class SDUNet(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.time_proj = Timesteps(320)
+        self.time_embedding = torch.nn.Sequential(
+            torch.nn.Linear(320, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
+
+        self.blocks = torch.nn.ModuleList([
+            # CrossAttnDownBlock2D
+            ResnetBlock(320, 320, 1280),
+            AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
+            PushBlock(),
+            ResnetBlock(320, 320, 1280),
+            AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
+            PushBlock(),
+            DownSampler(320),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(320, 640, 1280),
+            AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
+            PushBlock(),
+            ResnetBlock(640, 640, 1280),
+            AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
+            PushBlock(),
+            DownSampler(640),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(640, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
+            PushBlock(),
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
+            PushBlock(),
+            DownSampler(1280),
+            PushBlock(),
+            # DownBlock2D
+            ResnetBlock(1280, 1280, 1280),
+            PushBlock(),
+            ResnetBlock(1280, 1280, 1280),
+            PushBlock(),
+            # UNetMidBlock2DCrossAttn
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
+            ResnetBlock(1280, 1280, 1280),
+            # UpBlock2D
+            PopBlock(),
+            ResnetBlock(2560, 1280, 1280),
+            PopBlock(),
+            ResnetBlock(2560, 1280, 1280),
+            PopBlock(),
+            ResnetBlock(2560, 1280, 1280),
+            UpSampler(1280),
+            # CrossAttnUpBlock2D
+            PopBlock(),
+            ResnetBlock(2560, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
+            PopBlock(),
+            ResnetBlock(2560, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
+            PopBlock(),
+            ResnetBlock(1920, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
+            UpSampler(1280),
+            # CrossAttnUpBlock2D
+            PopBlock(),
+            ResnetBlock(1920, 640, 1280),
+            AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
+            PopBlock(),
+            ResnetBlock(1280, 640, 1280),
+            AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
+            PopBlock(),
+            ResnetBlock(960, 640, 1280),
+            AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
+            UpSampler(640),
+            # CrossAttnUpBlock2D
+            PopBlock(),
+            ResnetBlock(960, 320, 1280),
+            AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
+            PopBlock(),
+            ResnetBlock(640, 320, 1280),
+            AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
+            PopBlock(),
+            ResnetBlock(640, 320, 1280),
+            AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1)
+
+    def forward(self, sample, timestep, encoder_hidden_states, **kwargs):
+        # 1. time
+        time_emb = self.time_proj(timestep[None]).to(sample.dtype)
+        time_emb = self.time_embedding(time_emb)
+
+        # 2. pre-process
+        hidden_states = self.conv_in(sample)
+        text_emb = encoder_hidden_states
+        res_stack = [hidden_states]
+
+        # 3. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        
+        # 4. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+    
+    def state_dict_converter(self):
+        return SDUNetStateDictConverter()
+
+
+class SDUNetStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        # architecture
+        block_types = [
+            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
+            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
+            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
+            'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 
+            'ResnetBlock', 'AttentionBlock', 'ResnetBlock',
+            'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler',
+            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
+            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
+            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock'
+        ]
+
+        # Rename each parameter
+        name_list = sorted([name for name in state_dict])
+        rename_dict = {}
+        block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1}
+        last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""}
+        for name in name_list:
+            names = name.split(".")
+            if names[0] in ["conv_in", "conv_norm_out", "conv_out"]:
+                pass
+            elif names[0] in ["time_embedding", "add_embedding"]:
+                if names[0] == "add_embedding":
+                    names[0] = "add_time_embedding"
+                names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]]
+            elif names[0] in ["down_blocks", "mid_block", "up_blocks"]:
+                if names[0] == "mid_block":
+                    names.insert(1, "0")
+                block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]]
+                block_type_with_id = ".".join(names[:4])
+                if block_type_with_id != last_block_type_with_id[block_type]:
+                    block_id[block_type] += 1
+                last_block_type_with_id[block_type] = block_type_with_id
+                while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
+                    block_id[block_type] += 1
+                block_type_with_id = ".".join(names[:4])
+                names = ["blocks", str(block_id[block_type])] + names[4:]
+                if "ff" in names:
+                    ff_index = names.index("ff")
+                    component = ".".join(names[ff_index:ff_index+3])
+                    component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component]
+                    names = names[:ff_index] + [component] + names[ff_index+3:]
+                if "to_out" in names:
+                    names.pop(names.index("to_out") + 1)
+            else:
+                raise ValueError(f"Unknown parameters: {name}")
+            rename_dict[name] = ".".join(names)
+
+        # Convert state_dict
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if ".proj_in." in name or ".proj_out." in name:
+                param = param.squeeze()
+            state_dict_[rename_dict[name]] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias",
+            "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight",
+            "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias",
+            "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight",
+            "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias",
+            "model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight",
+            "model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias",
+            "model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight",
+            "model.diffusion_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias",
+            "model.diffusion_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight",
+            "model.diffusion_model.input_blocks.1.1.norm.bias": "blocks.1.norm.bias",
+            "model.diffusion_model.input_blocks.1.1.norm.weight": "blocks.1.norm.weight",
+            "model.diffusion_model.input_blocks.1.1.proj_in.bias": "blocks.1.proj_in.bias",
+            "model.diffusion_model.input_blocks.1.1.proj_in.weight": "blocks.1.proj_in.weight",
+            "model.diffusion_model.input_blocks.1.1.proj_out.bias": "blocks.1.proj_out.bias",
+            "model.diffusion_model.input_blocks.1.1.proj_out.weight": "blocks.1.proj_out.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.1.transformer_blocks.0.attn1.to_k.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.1.transformer_blocks.0.attn1.to_out.bias",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.1.transformer_blocks.0.attn1.to_out.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.1.transformer_blocks.0.attn1.to_q.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.1.transformer_blocks.0.attn1.to_v.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "blocks.1.transformer_blocks.0.attn2.to_k.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.1.transformer_blocks.0.attn2.to_out.bias",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.1.transformer_blocks.0.attn2.to_out.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight": "blocks.1.transformer_blocks.0.attn2.to_q.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight": "blocks.1.transformer_blocks.0.attn2.to_v.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.1.transformer_blocks.0.act_fn.proj.bias",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.1.transformer_blocks.0.act_fn.proj.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "blocks.1.transformer_blocks.0.ff.bias",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "blocks.1.transformer_blocks.0.ff.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.bias": "blocks.1.transformer_blocks.0.norm1.bias",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "blocks.1.transformer_blocks.0.norm1.weight",
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+            "model.diffusion_model.output_blocks.7.1.transformer_blocks.0.ff.net.2.bias": "blocks.53.transformer_blocks.0.ff.bias",
+            "model.diffusion_model.output_blocks.7.1.transformer_blocks.0.ff.net.2.weight": "blocks.53.transformer_blocks.0.ff.weight",
+            "model.diffusion_model.output_blocks.7.1.transformer_blocks.0.norm1.bias": "blocks.53.transformer_blocks.0.norm1.bias",
+            "model.diffusion_model.output_blocks.7.1.transformer_blocks.0.norm1.weight": "blocks.53.transformer_blocks.0.norm1.weight",
+            "model.diffusion_model.output_blocks.7.1.transformer_blocks.0.norm2.bias": "blocks.53.transformer_blocks.0.norm2.bias",
+            "model.diffusion_model.output_blocks.7.1.transformer_blocks.0.norm2.weight": "blocks.53.transformer_blocks.0.norm2.weight",
+            "model.diffusion_model.output_blocks.7.1.transformer_blocks.0.norm3.bias": "blocks.53.transformer_blocks.0.norm3.bias",
+            "model.diffusion_model.output_blocks.7.1.transformer_blocks.0.norm3.weight": "blocks.53.transformer_blocks.0.norm3.weight",
+            "model.diffusion_model.output_blocks.8.0.emb_layers.1.bias": "blocks.55.time_emb_proj.bias",
+            "model.diffusion_model.output_blocks.8.0.emb_layers.1.weight": "blocks.55.time_emb_proj.weight",
+            "model.diffusion_model.output_blocks.8.0.in_layers.0.bias": "blocks.55.norm1.bias",
+            "model.diffusion_model.output_blocks.8.0.in_layers.0.weight": "blocks.55.norm1.weight",
+            "model.diffusion_model.output_blocks.8.0.in_layers.2.bias": "blocks.55.conv1.bias",
+            "model.diffusion_model.output_blocks.8.0.in_layers.2.weight": "blocks.55.conv1.weight",
+            "model.diffusion_model.output_blocks.8.0.out_layers.0.bias": "blocks.55.norm2.bias",
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+            "model.diffusion_model.output_blocks.8.0.out_layers.3.weight": "blocks.55.conv2.weight",
+            "model.diffusion_model.output_blocks.8.0.skip_connection.bias": "blocks.55.conv_shortcut.bias",
+            "model.diffusion_model.output_blocks.8.0.skip_connection.weight": "blocks.55.conv_shortcut.weight",
+            "model.diffusion_model.output_blocks.8.1.norm.bias": "blocks.56.norm.bias",
+            "model.diffusion_model.output_blocks.8.1.norm.weight": "blocks.56.norm.weight",
+            "model.diffusion_model.output_blocks.8.1.proj_in.bias": "blocks.56.proj_in.bias",
+            "model.diffusion_model.output_blocks.8.1.proj_in.weight": "blocks.56.proj_in.weight",
+            "model.diffusion_model.output_blocks.8.1.proj_out.bias": "blocks.56.proj_out.bias",
+            "model.diffusion_model.output_blocks.8.1.proj_out.weight": "blocks.56.proj_out.weight",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.attn1.to_k.weight": "blocks.56.transformer_blocks.0.attn1.to_k.weight",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.56.transformer_blocks.0.attn1.to_out.bias",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.56.transformer_blocks.0.attn1.to_out.weight",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.attn1.to_q.weight": "blocks.56.transformer_blocks.0.attn1.to_q.weight",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.attn1.to_v.weight": "blocks.56.transformer_blocks.0.attn1.to_v.weight",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.attn2.to_k.weight": "blocks.56.transformer_blocks.0.attn2.to_k.weight",
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+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.attn2.to_q.weight": "blocks.56.transformer_blocks.0.attn2.to_q.weight",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.attn2.to_v.weight": "blocks.56.transformer_blocks.0.attn2.to_v.weight",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.56.transformer_blocks.0.act_fn.proj.bias",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.56.transformer_blocks.0.act_fn.proj.weight",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.ff.net.2.bias": "blocks.56.transformer_blocks.0.ff.bias",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.ff.net.2.weight": "blocks.56.transformer_blocks.0.ff.weight",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.norm1.bias": "blocks.56.transformer_blocks.0.norm1.bias",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.norm1.weight": "blocks.56.transformer_blocks.0.norm1.weight",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.norm2.bias": "blocks.56.transformer_blocks.0.norm2.bias",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.norm2.weight": "blocks.56.transformer_blocks.0.norm2.weight",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.norm3.bias": "blocks.56.transformer_blocks.0.norm3.bias",
+            "model.diffusion_model.output_blocks.8.1.transformer_blocks.0.norm3.weight": "blocks.56.transformer_blocks.0.norm3.weight",
+            "model.diffusion_model.output_blocks.8.2.conv.bias": "blocks.57.conv.bias",
+            "model.diffusion_model.output_blocks.8.2.conv.weight": "blocks.57.conv.weight",
+            "model.diffusion_model.output_blocks.9.0.emb_layers.1.bias": "blocks.59.time_emb_proj.bias",
+            "model.diffusion_model.output_blocks.9.0.emb_layers.1.weight": "blocks.59.time_emb_proj.weight",
+            "model.diffusion_model.output_blocks.9.0.in_layers.0.bias": "blocks.59.norm1.bias",
+            "model.diffusion_model.output_blocks.9.0.in_layers.0.weight": "blocks.59.norm1.weight",
+            "model.diffusion_model.output_blocks.9.0.in_layers.2.bias": "blocks.59.conv1.bias",
+            "model.diffusion_model.output_blocks.9.0.in_layers.2.weight": "blocks.59.conv1.weight",
+            "model.diffusion_model.output_blocks.9.0.out_layers.0.bias": "blocks.59.norm2.bias",
+            "model.diffusion_model.output_blocks.9.0.out_layers.0.weight": "blocks.59.norm2.weight",
+            "model.diffusion_model.output_blocks.9.0.out_layers.3.bias": "blocks.59.conv2.bias",
+            "model.diffusion_model.output_blocks.9.0.out_layers.3.weight": "blocks.59.conv2.weight",
+            "model.diffusion_model.output_blocks.9.0.skip_connection.bias": "blocks.59.conv_shortcut.bias",
+            "model.diffusion_model.output_blocks.9.0.skip_connection.weight": "blocks.59.conv_shortcut.weight",
+            "model.diffusion_model.output_blocks.9.1.norm.bias": "blocks.60.norm.bias",
+            "model.diffusion_model.output_blocks.9.1.norm.weight": "blocks.60.norm.weight",
+            "model.diffusion_model.output_blocks.9.1.proj_in.bias": "blocks.60.proj_in.bias",
+            "model.diffusion_model.output_blocks.9.1.proj_in.weight": "blocks.60.proj_in.weight",
+            "model.diffusion_model.output_blocks.9.1.proj_out.bias": "blocks.60.proj_out.bias",
+            "model.diffusion_model.output_blocks.9.1.proj_out.weight": "blocks.60.proj_out.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn1.to_k.weight": "blocks.60.transformer_blocks.0.attn1.to_k.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.60.transformer_blocks.0.attn1.to_out.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.60.transformer_blocks.0.attn1.to_out.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn1.to_q.weight": "blocks.60.transformer_blocks.0.attn1.to_q.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn1.to_v.weight": "blocks.60.transformer_blocks.0.attn1.to_v.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_k.weight": "blocks.60.transformer_blocks.0.attn2.to_k.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.60.transformer_blocks.0.attn2.to_out.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.60.transformer_blocks.0.attn2.to_out.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_q.weight": "blocks.60.transformer_blocks.0.attn2.to_q.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_v.weight": "blocks.60.transformer_blocks.0.attn2.to_v.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.60.transformer_blocks.0.act_fn.proj.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.60.transformer_blocks.0.act_fn.proj.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.2.bias": "blocks.60.transformer_blocks.0.ff.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.2.weight": "blocks.60.transformer_blocks.0.ff.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm1.bias": "blocks.60.transformer_blocks.0.norm1.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm1.weight": "blocks.60.transformer_blocks.0.norm1.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.bias": "blocks.60.transformer_blocks.0.norm2.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.weight": "blocks.60.transformer_blocks.0.norm2.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.bias": "blocks.60.transformer_blocks.0.norm3.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.weight": "blocks.60.transformer_blocks.0.norm3.weight",
+            "model.diffusion_model.time_embed.0.bias": "time_embedding.0.bias",
+            "model.diffusion_model.time_embed.0.weight": "time_embedding.0.weight",
+            "model.diffusion_model.time_embed.2.bias": "time_embedding.2.bias",
+            "model.diffusion_model.time_embed.2.weight": "time_embedding.2.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if ".proj_in." in name or ".proj_out." in name:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
\ No newline at end of file