modelscope/DiffSynth-Painter

1import torch2from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, PopBlock, DownSampler, UpSampler3 4 5class SDXLUNet(torch.nn.Module):6    def __init__(self, is_kolors=False):7        super().__init__()8        self.time_proj = Timesteps(320)9        self.time_embedding = torch.nn.Sequential(10            torch.nn.Linear(320, 1280),11            torch.nn.SiLU(),12            torch.nn.Linear(1280, 1280)13        )14        self.add_time_proj = Timesteps(256)15        self.add_time_embedding = torch.nn.Sequential(16            torch.nn.Linear(5632 if is_kolors else 2816, 1280),17            torch.nn.SiLU(),18            torch.nn.Linear(1280, 1280)19        )20        self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)21        self.text_intermediate_proj = torch.nn.Linear(4096, 2048) if is_kolors else None22 23        self.blocks = torch.nn.ModuleList([24            # DownBlock2D25            ResnetBlock(320, 320, 1280),26            PushBlock(),27            ResnetBlock(320, 320, 1280),28            PushBlock(),29            DownSampler(320),30            PushBlock(),31            # CrossAttnDownBlock2D32            ResnetBlock(320, 640, 1280),33            AttentionBlock(10, 64, 640, 2, 2048),34            PushBlock(),35            ResnetBlock(640, 640, 1280),36            AttentionBlock(10, 64, 640, 2, 2048),37            PushBlock(),38            DownSampler(640),39            PushBlock(),40            # CrossAttnDownBlock2D41            ResnetBlock(640, 1280, 1280),42            AttentionBlock(20, 64, 1280, 10, 2048),43            PushBlock(),44            ResnetBlock(1280, 1280, 1280),45            AttentionBlock(20, 64, 1280, 10, 2048),46            PushBlock(),47            # UNetMidBlock2DCrossAttn48            ResnetBlock(1280, 1280, 1280),49            AttentionBlock(20, 64, 1280, 10, 2048),50            ResnetBlock(1280, 1280, 1280),51            # CrossAttnUpBlock2D52            PopBlock(),53            ResnetBlock(2560, 1280, 1280),54            AttentionBlock(20, 64, 1280, 10, 2048),55            PopBlock(),56            ResnetBlock(2560, 1280, 1280),57            AttentionBlock(20, 64, 1280, 10, 2048),58            PopBlock(),59            ResnetBlock(1920, 1280, 1280),60            AttentionBlock(20, 64, 1280, 10, 2048),61            UpSampler(1280),62            # CrossAttnUpBlock2D63            PopBlock(),64            ResnetBlock(1920, 640, 1280),65            AttentionBlock(10, 64, 640, 2, 2048),66            PopBlock(),67            ResnetBlock(1280, 640, 1280),68            AttentionBlock(10, 64, 640, 2, 2048),69            PopBlock(),70            ResnetBlock(960, 640, 1280),71            AttentionBlock(10, 64, 640, 2, 2048),72            UpSampler(640),73            # UpBlock2D74            PopBlock(),75            ResnetBlock(960, 320, 1280),76            PopBlock(),77            ResnetBlock(640, 320, 1280),78            PopBlock(),79            ResnetBlock(640, 320, 1280)80        ])81 82        self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5)83        self.conv_act = torch.nn.SiLU()84        self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1)85 86        self.is_kolors = is_kolors87 88    def forward(89        self,90        sample, timestep, encoder_hidden_states, add_time_id, add_text_embeds,91        tiled=False, tile_size=64, tile_stride=8,92        use_gradient_checkpointing=False,93        **kwargs94    ):95        # 1. time96        t_emb = self.time_proj(timestep).to(sample.dtype)97        t_emb = self.time_embedding(t_emb)98        99        time_embeds = self.add_time_proj(add_time_id)100        time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1))101        add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1)102        add_embeds = add_embeds.to(sample.dtype)103        add_embeds = self.add_time_embedding(add_embeds)104 105        time_emb = t_emb + add_embeds106        107        # 2. pre-process108        height, width = sample.shape[2], sample.shape[3]109        hidden_states = self.conv_in(sample)110        text_emb = encoder_hidden_states if self.text_intermediate_proj is None else self.text_intermediate_proj(encoder_hidden_states)111        res_stack = [hidden_states]112        113        # 3. blocks114        def create_custom_forward(module):115            def custom_forward(*inputs):116                return module(*inputs)117            return custom_forward118        for i, block in enumerate(self.blocks):119            if self.training and use_gradient_checkpointing and not (isinstance(block, PushBlock) or isinstance(block, PopBlock)):120                hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint(121                    create_custom_forward(block),122                    hidden_states, time_emb, text_emb, res_stack,123                    use_reentrant=False,124                )125            else:126                hidden_states, time_emb, text_emb, res_stack = block(127                    hidden_states, time_emb, text_emb, res_stack,128                    tiled=tiled, tile_size=tile_size, tile_stride=tile_stride129                )130        131        # 4. output132        hidden_states = self.conv_norm_out(hidden_states)133        hidden_states = self.conv_act(hidden_states)134        hidden_states = self.conv_out(hidden_states)135 136        return hidden_states137    138    @staticmethod139    def state_dict_converter():140        return SDXLUNetStateDictConverter()141 142 143class SDXLUNetStateDictConverter:144    def __init__(self):145        pass146 147    def from_diffusers(self, state_dict):148        # architecture149        block_types = [150            'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 'DownSampler', 'PushBlock',151            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',152            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock',153            'ResnetBlock', 'AttentionBlock', 'ResnetBlock',154            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',155            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',156            'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock'157        ]158 159        # Rename each parameter160        name_list = sorted([name for name in state_dict])161        rename_dict = {}162        block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1}163        last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""}164        for name in name_list:165            names = name.split(".")166            if names[0] in ["conv_in", "conv_norm_out", "conv_out"]:167                pass168            elif names[0] in ["encoder_hid_proj"]:169                names[0] = "text_intermediate_proj"170            elif names[0] in ["time_embedding", "add_embedding"]:171                if names[0] == "add_embedding":172                    names[0] = "add_time_embedding"173                names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]]174            elif names[0] in ["down_blocks", "mid_block", "up_blocks"]:175                if names[0] == "mid_block":176                    names.insert(1, "0")177                block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]]178                block_type_with_id = ".".join(names[:4])179                if block_type_with_id != last_block_type_with_id[block_type]:180                    block_id[block_type] += 1181                last_block_type_with_id[block_type] = block_type_with_id182                while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:183                    block_id[block_type] += 1184                block_type_with_id = ".".join(names[:4])185                names = ["blocks", str(block_id[block_type])] + names[4:]186                if "ff" in names:187                    ff_index = names.index("ff")188                    component = ".".join(names[ff_index:ff_index+3])189                    component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component]190                    names = names[:ff_index] + [component] + names[ff_index+3:]191                if "to_out" in names:192                    names.pop(names.index("to_out") + 1)193            else:194                raise ValueError(f"Unknown parameters: {name}")195            rename_dict[name] = ".".join(names)196 197        # Convert state_dict198        state_dict_ = {}199        for name, param in state_dict.items():200            if ".proj_in." in name or ".proj_out." in name:201                param = param.squeeze()202            state_dict_[rename_dict[name]] = param203        if "text_intermediate_proj.weight" in state_dict_:204            return state_dict_, {"is_kolors": True}205        else:206            return state_dict_207    208    def from_civitai(self, state_dict):209        rename_dict = {210            "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias",211            "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight",212            "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias",213            "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight",214            "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias",215            "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight",216            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