diffsynth/models/sdxl_unet.py

import torch
from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, PopBlock, DownSampler, UpSampler


class SDXLUNet(torch.nn.Module):
    def __init__(self, is_kolors=False):
        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.add_time_proj = Timesteps(256)
        self.add_time_embedding = torch.nn.Sequential(
            torch.nn.Linear(5632 if is_kolors else 2816, 1280),
            torch.nn.SiLU(),
            torch.nn.Linear(1280, 1280)
        )
        self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
        self.text_intermediate_proj = torch.nn.Linear(4096, 2048) if is_kolors else None

        self.blocks = torch.nn.ModuleList([
            # DownBlock2D
            ResnetBlock(320, 320, 1280),
            PushBlock(),
            ResnetBlock(320, 320, 1280),
            PushBlock(),
            DownSampler(320),
            PushBlock(),
            # CrossAttnDownBlock2D
            ResnetBlock(320, 640, 1280),
            AttentionBlock(10, 64, 640, 2, 2048),
            PushBlock(),
            ResnetBlock(640, 640, 1280),
            AttentionBlock(10, 64, 640, 2, 2048),
            PushBlock(),
            DownSampler(640),
            PushBlock(),
            # CrossAttnDownBlock2D
            ResnetBlock(640, 1280, 1280),
            AttentionBlock(20, 64, 1280, 10, 2048),
            PushBlock(),
            ResnetBlock(1280, 1280, 1280),
            AttentionBlock(20, 64, 1280, 10, 2048),
            PushBlock(),
            # UNetMidBlock2DCrossAttn
            ResnetBlock(1280, 1280, 1280),
            AttentionBlock(20, 64, 1280, 10, 2048),
            ResnetBlock(1280, 1280, 1280),
            # CrossAttnUpBlock2D
            PopBlock(),
            ResnetBlock(2560, 1280, 1280),
            AttentionBlock(20, 64, 1280, 10, 2048),
            PopBlock(),
            ResnetBlock(2560, 1280, 1280),
            AttentionBlock(20, 64, 1280, 10, 2048),
            PopBlock(),
            ResnetBlock(1920, 1280, 1280),
            AttentionBlock(20, 64, 1280, 10, 2048),
            UpSampler(1280),
            # CrossAttnUpBlock2D
            PopBlock(),
            ResnetBlock(1920, 640, 1280),
            AttentionBlock(10, 64, 640, 2, 2048),
            PopBlock(),
            ResnetBlock(1280, 640, 1280),
            AttentionBlock(10, 64, 640, 2, 2048),
            PopBlock(),
            ResnetBlock(960, 640, 1280),
            AttentionBlock(10, 64, 640, 2, 2048),
            UpSampler(640),
            # UpBlock2D
            PopBlock(),
            ResnetBlock(960, 320, 1280),
            PopBlock(),
            ResnetBlock(640, 320, 1280),
            PopBlock(),
            ResnetBlock(640, 320, 1280)
        ])

        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, add_time_id, add_text_embeds,
        tiled=False, tile_size=64, tile_stride=8,
        use_gradient_checkpointing=False,
        **kwargs
    ):
        # 1. time
        t_emb = self.time_proj(timestep[None]).to(sample.dtype)
        t_emb = self.time_embedding(t_emb)
        
        time_embeds = self.add_time_proj(add_time_id)
        time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1))
        add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1)
        add_embeds = add_embeds.to(sample.dtype)
        add_embeds = self.add_time_embedding(add_embeds)

        time_emb = t_emb + add_embeds
        
        # 2. pre-process
        height, width = sample.shape[2], sample.shape[3]
        hidden_states = self.conv_in(sample)
        text_emb = encoder_hidden_states if self.text_intermediate_proj is None else self.text_intermediate_proj(encoder_hidden_states)
        res_stack = [hidden_states]
        
        # 3. blocks
        def create_custom_forward(module):
            def custom_forward(*inputs):
                return module(*inputs)
            return custom_forward
        for i, block in enumerate(self.blocks):
            if self.training and use_gradient_checkpointing and not (isinstance(block, PushBlock) or isinstance(block, PopBlock)):
                hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states, time_emb, text_emb, res_stack,
                    use_reentrant=False,
                )
            else:
                hidden_states, time_emb, text_emb, res_stack = block(
                    hidden_states, time_emb, text_emb, res_stack,
                    tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
                )
        
        # 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 SDXLUNetStateDictConverter()


class SDXLUNetStateDictConverter:
    def __init__(self):
        pass

    def from_diffusers(self, state_dict):
        # architecture
        block_types = [
            'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 'DownSampler', 'PushBlock',
            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock',
            'ResnetBlock', 'AttentionBlock', 'ResnetBlock',
            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
            'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock'
        ]

        # 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 ["encoder_hid_proj"]:
                names[0] = "text_intermediate_proj"
            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 = {
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            "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.2.0.emb_layers.1.bias": "blocks.2.time_emb_proj.bias",
            "model.diffusion_model.input_blocks.2.0.emb_layers.1.weight": "blocks.2.time_emb_proj.weight",
            "model.diffusion_model.input_blocks.2.0.in_layers.0.bias": "blocks.2.norm1.bias",
            "model.diffusion_model.input_blocks.2.0.in_layers.0.weight": "blocks.2.norm1.weight",
            "model.diffusion_model.input_blocks.2.0.in_layers.2.bias": "blocks.2.conv1.bias",
            "model.diffusion_model.input_blocks.2.0.in_layers.2.weight": "blocks.2.conv1.weight",
            "model.diffusion_model.input_blocks.2.0.out_layers.0.bias": "blocks.2.norm2.bias",
            "model.diffusion_model.input_blocks.2.0.out_layers.0.weight": "blocks.2.norm2.weight",
            "model.diffusion_model.input_blocks.2.0.out_layers.3.bias": "blocks.2.conv2.bias",
            "model.diffusion_model.input_blocks.2.0.out_layers.3.weight": "blocks.2.conv2.weight",
            "model.diffusion_model.input_blocks.3.0.op.bias": "blocks.4.conv.bias",
            "model.diffusion_model.input_blocks.3.0.op.weight": "blocks.4.conv.weight",
            "model.diffusion_model.input_blocks.4.0.emb_layers.1.bias": "blocks.6.time_emb_proj.bias",
            "model.diffusion_model.input_blocks.4.0.emb_layers.1.weight": "blocks.6.time_emb_proj.weight",
            "model.diffusion_model.input_blocks.4.0.in_layers.0.bias": "blocks.6.norm1.bias",
            "model.diffusion_model.input_blocks.4.0.in_layers.0.weight": "blocks.6.norm1.weight",
            "model.diffusion_model.input_blocks.4.0.in_layers.2.bias": "blocks.6.conv1.bias",
            "model.diffusion_model.input_blocks.4.0.in_layers.2.weight": "blocks.6.conv1.weight",
            "model.diffusion_model.input_blocks.4.0.out_layers.0.bias": "blocks.6.norm2.bias",
            "model.diffusion_model.input_blocks.4.0.out_layers.0.weight": "blocks.6.norm2.weight",
            "model.diffusion_model.input_blocks.4.0.out_layers.3.bias": "blocks.6.conv2.bias",
            "model.diffusion_model.input_blocks.4.0.out_layers.3.weight": "blocks.6.conv2.weight",
            "model.diffusion_model.input_blocks.4.0.skip_connection.bias": "blocks.6.conv_shortcut.bias",
            "model.diffusion_model.input_blocks.4.0.skip_connection.weight": "blocks.6.conv_shortcut.weight",
            "model.diffusion_model.input_blocks.4.1.norm.bias": "blocks.7.norm.bias",
            "model.diffusion_model.input_blocks.4.1.norm.weight": "blocks.7.norm.weight",
            "model.diffusion_model.input_blocks.4.1.proj_in.bias": "blocks.7.proj_in.bias",
            "model.diffusion_model.input_blocks.4.1.proj_in.weight": "blocks.7.proj_in.weight",
            "model.diffusion_model.input_blocks.4.1.proj_out.bias": "blocks.7.proj_out.bias",
            "model.diffusion_model.input_blocks.4.1.proj_out.weight": "blocks.7.proj_out.weight",
            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_k.weight": "blocks.7.transformer_blocks.0.attn1.to_k.weight",
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            "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_