SDXLで高解像度での構図の破綻を軽減する

forward_of_sdxl_original_unet.py

    def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
        # broadcast timesteps to batch dimension
        timesteps = timesteps.expand(x.shape[0])

        hs = []
        t_emb = get_timestep_embedding(timesteps, self.model_channels)  # , repeat_only=False)
        t_emb = t_emb.to(x.dtype)
        emb = self.time_embed(t_emb)

        assert x.shape[0] == y.shape[0], f"batch size mismatch: {x.shape[0]} != {y.shape[0]}"
        assert x.dtype == y.dtype, f"dtype mismatch: {x.dtype} != {y.dtype}"
        # assert x.dtype == self.dtype
        emb = emb + self.label_emb(y)

        def call_module(module, h, emb, context):
            x = h
            for layer in module:
                # print(layer.__class__.__name__, x.dtype, emb.dtype, context.dtype if context is not None else None)
                if isinstance(layer, ResnetBlock2D):
                    x = layer(x, emb)
                elif isinstance(layer, Transformer2DModel):
                    x = layer(x, context)
                else:
                    x = layer(x)
            return x

        # h = x.type(self.dtype)
        h = x

        # downsample depth
        # 深くすると全体の構図は安定するが、キャラがゆがむ。浅くしすぎると細部が混沌とする
        ds_depth_1 = 3 # 2~4 くらいがよさそう
        ds_depth_2 = 3 # depth_1より+0~+2くらいがよさそう
        # downsample timestep
        # 大きくすると構図が乱れて、小さくするとディテールが乱れる
        ds_timestep_1 = 900
        ds_timestep_2 = 650 # timestep_1より小さいこと。デッサンに影響する 

        depth = 0  # current depth
        for module in self.input_blocks:
            h = call_module(module, h, emb, context)
            hs.append(h)

            # print(depth, h.shape, timesteps)
            if (depth == ds_depth_1 and timesteps[0] > ds_timestep_1) or (
                depth == ds_depth_2 and ds_timestep_1 > timesteps[0] and timesteps[0] > ds_timestep_2
            ):
                # bicubicでないとゆがむ、align_cornersはあまり影響しない模様
                h = F.interpolate(h.float(), scale_factor=0.5, mode="bicubic", align_corners=False).to(h.dtype)  # bfloat16対応
            depth += 1

        h = call_module(self.middle_block, h, emb, context)

        for module in self.output_blocks:
            depth -= 1
            # print(depth, h.shape)
            if (depth == ds_depth_1 and timesteps[0] > ds_timestep_1) or (
                depth == ds_depth_2 and ds_timestep_1 > timesteps[0] and timesteps[0] > ds_timestep_2
            ):
                h = F.interpolate(h.float(), scale_factor=2.0, mode="bicubic", align_corners=False).to(h.dtype)  # bfloat16対応

            h = torch.cat([h, hs.pop()], dim=1)
            h = call_module(module, h, emb, context)

        h = h.type(x.dtype)
        h = call_module(self.out, h, emb, context)

        return h

Mr Kohya, please advise how to implement this at the model level. I would like to try to assemble a checkpoint with a built-in unet shrink for 1.5 checkpoint..