extensionsa/multidiffusion-upscaler-for-automatic1111/tile_methods/multidiffusion.py

from tile_methods.abstractdiffusion import AbstractDiffusion
from tile_utils.utils import *


class MultiDiffusion(AbstractDiffusion):
    """
        Multi-Diffusion Implementation
        https://arxiv.org/abs/2302.08113
    """

    def __init__(self, p:Processing, *args, **kwargs):
        super().__init__(p, *args, **kwargs)
        assert p.sampler_name != 'UniPC', 'MultiDiffusion is not compatible with UniPC!'

    def hook(self):
        if self.is_kdiff:
            # For K-Diffusion sampler with uniform prompt, we hijack into the inner model for simplicity
            # Otherwise, the masked-redraw will break due to the init_latent
            self.sampler: KDiffusionSampler
            self.sampler.model_wrap_cfg: CFGDenoiserKDiffusion
            self.sampler.model_wrap_cfg.inner_model: Union[CompVisDenoiser, CompVisVDenoiser]
            self.sampler_forward = self.sampler.model_wrap_cfg.inner_model.forward
            self.sampler.model_wrap_cfg.inner_model.forward = self.kdiff_forward
        else:
            self.sampler: CompVisSampler
            self.sampler.model_wrap_cfg: CFGDenoiserTimesteps
            self.sampler.model_wrap_cfg.inner_model: Union[CompVisTimestepsDenoiser, CompVisTimestepsVDenoiser]
            self.sampler_forward = self.sampler.model_wrap_cfg.inner_model.forward
            self.sampler.model_wrap_cfg.inner_model.forward = self.ddim_forward

    @staticmethod
    def unhook():
        # no need to unhook MultiDiffusion as it only hook the sampler,
        # which will be destroyed after the painting is done
        pass

    def reset_buffer(self, x_in:Tensor):
        super().reset_buffer(x_in)

    @custom_bbox
    def init_custom_bbox(self, *args):
        super().init_custom_bbox(*args)

        for bbox in self.custom_bboxes:
            if bbox.blend_mode == BlendMode.BACKGROUND:
                self.weights[bbox.slicer] += 1.0

    ''' ↓↓↓ kernel hijacks ↓↓↓ '''

    @torch.no_grad()
    @keep_signature
    def kdiff_forward(self, x_in:Tensor, sigma_in:Tensor, cond:CondDict) -> Tensor:
        assert CompVisDenoiser.forward
        assert CompVisVDenoiser.forward

        def org_func(x:Tensor) -> Tensor:
            return self.sampler_forward(x, sigma_in, cond=cond)

        def repeat_func(x_tile:Tensor, bboxes:List[CustomBBox]) -> Tensor:
            # For kdiff sampler, the dim 0 of input x_in is:
            #   = batch_size * (num_AND + 1)   if not an edit model
            #   = batch_size * (num_AND + 2)   otherwise
            sigma_tile = self.repeat_tensor(sigma_in, len(bboxes))
            cond_tile = self.repeat_cond_dict(cond, bboxes)
            return self.sampler_forward(x_tile, sigma_tile, cond=cond_tile)

        def custom_func(x:Tensor, bbox_id:int, bbox:CustomBBox) -> Tensor:
            return self.kdiff_custom_forward(x, sigma_in, cond, bbox_id, bbox, self.sampler_forward)

        return self.sample_one_step(x_in, org_func, repeat_func, custom_func)

    @torch.no_grad()
    @keep_signature
    def ddim_forward(self, x_in:Tensor, ts_in:Tensor, cond:Union[CondDict, Tensor]) -> Tensor:
        assert CompVisTimestepsDenoiser.forward
        assert CompVisTimestepsVDenoiser.forward

        def org_func(x:Tensor) -> Tensor:
            return self.sampler_forward(x, ts_in, cond=cond)

        def repeat_func(x_tile:Tensor, bboxes:List[CustomBBox]) -> Tuple[Tensor, Tensor]:
            n_rep = len(bboxes)
            ts_tile = self.repeat_tensor(ts_in, n_rep)
            if isinstance(cond, dict):   # FIXME: when will enter this branch?
                cond_tile = self.repeat_cond_dict(cond, bboxes)
            else:
                cond_tile = self.repeat_tensor(cond, n_rep)
            return self.sampler_forward(x_tile, ts_tile, cond=cond_tile)

        def custom_func(x:Tensor, bbox_id:int, bbox:CustomBBox) -> Tensor:
            # before the final forward, we can set the control tensor
            def forward_func(x, *args, **kwargs):
                self.set_custom_controlnet_tensors(bbox_id, 2*x.shape[0])
                self.set_custom_stablesr_tensors(bbox_id)
                return self.sampler_forward(x, *args, **kwargs)
            return self.ddim_custom_forward(x, cond, bbox, ts_in, forward_func)

        return self.sample_one_step(x_in, org_func, repeat_func, custom_func)

    def repeat_tensor(self, x:Tensor, n:int) -> Tensor:
        ''' repeat the tensor on it's first dim '''
        if n == 1: return x
        B = x.shape[0]
        r_dims = len(x.shape) - 1
        if B == 1:      # batch_size = 1 (not `tile_batch_size`)
            shape = [n] + [-1] * r_dims     # [N, -1, ...]
            return x.expand(shape)          # `expand` is much lighter than `tile`
        else:
            shape = [n] + [1] * r_dims      # [N, 1, ...]
            return x.repeat(shape)

    def repeat_cond_dict(self, cond_in:CondDict, bboxes:List[CustomBBox]) -> CondDict:
        ''' repeat all tensors in cond_dict on it's first dim (for a batch of tiles), returns a new object '''
        # n_repeat
        n_rep = len(bboxes)
        # txt cond
        tcond = self.get_tcond(cond_in)           # [B=1, L, D] => [B*N, L, D]
        tcond = self.repeat_tensor(tcond, n_rep)
        # img cond
        icond = self.get_icond(cond_in)
        if icond.shape[2:] == (self.h, self.w):   # img2img, [B=1, C, H, W]
            icond = torch.cat([icond[bbox.slicer] for bbox in bboxes], dim=0)
        else:                                     # txt2img, [B=1, C=5, H=1, W=1]
            icond = self.repeat_tensor(icond, n_rep)
        # vec cond (SDXL)
        vcond = self.get_vcond(cond_in)           # [B=1, D]
        if vcond is not None:
            vcond = self.repeat_tensor(vcond, n_rep)  # [B*N, D]
        return self.make_cond_dict(cond_in, tcond, icond, vcond)

    def sample_one_step(self, x_in:Tensor, org_func:Callable, repeat_func:Callable, custom_func:Callable) -> Tensor:
        '''
        this method splits the whole latent and process in tiles
            - x_in: current whole U-Net latent
            - org_func: original forward function, when use highres
            - repeat_func: one step denoiser for grid tile
            - custom_func: one step denoiser for custom tile
        '''

        N, C, H, W = x_in.shape
        if (H, W) != (self.h, self.w):
            # We don't tile highres, let's just use the original org_func
            self.reset_controlnet_tensors()
            return org_func(x_in)

        # clear buffer canvas
        self.reset_buffer(x_in)

        # Background sampling (grid bbox)
        if self.draw_background:
            for batch_id, bboxes in enumerate(self.batched_bboxes):
                if state.interrupted: return x_in

                # batching
                x_tile = torch.cat([x_in[bbox.slicer] for bbox in bboxes], dim=0)   # [TB, C, TH, TW]

                # controlnet tiling
                # FIXME: is_denoise is default to False, however it is set to True in case of MixtureOfDiffusers, why?
                self.switch_controlnet_tensors(batch_id, N, len(bboxes))

                # stablesr tiling
                self.switch_stablesr_tensors(batch_id)

                # compute tiles
                x_tile_out = repeat_func(x_tile, bboxes)
                for i, bbox in enumerate(bboxes):
                    self.x_buffer[bbox.slicer] += x_tile_out[i*N:(i+1)*N, :, :, :]

                # update progress bar
                self.update_pbar()

        # Custom region sampling (custom bbox)
        x_feather_buffer = None
        x_feather_mask   = None
        x_feather_count  = None
        if len(self.custom_bboxes) > 0:
            for bbox_id, bbox in enumerate(self.custom_bboxes):
                if state.interrupted: return x_in

                if not self.p.disable_extra_networks:
                    with devices.autocast():
                        extra_networks.activate(self.p, bbox.extra_network_data)

                x_tile = x_in[bbox.slicer]

                # retrieve original x_in from construncted input
                x_tile_out = custom_func(x_tile, bbox_id, bbox)

                if bbox.blend_mode == BlendMode.BACKGROUND:
                    self.x_buffer[bbox.slicer] += x_tile_out
                elif bbox.blend_mode == BlendMode.FOREGROUND:
                    if x_feather_buffer is None:
                        x_feather_buffer = torch.zeros_like(self.x_buffer)
                        x_feather_mask   = torch.zeros((1, 1, H, W), device=x_in.device)
                        x_feather_count  = torch.zeros((1, 1, H, W), device=x_in.device)
                    x_feather_buffer[bbox.slicer] += x_tile_out
                    x_feather_mask  [bbox.slicer] += bbox.feather_mask
                    x_feather_count [bbox.slicer] += 1

                if not self.p.disable_extra_networks:
                    with devices.autocast():
                        extra_networks.deactivate(self.p, bbox.extra_network_data)

                # update progress bar
                self.update_pbar()

        # Averaging background buffer
        x_out = torch.where(self.weights > 1, self.x_buffer / self.weights, self.x_buffer)

        # Foreground Feather blending
        if x_feather_buffer is not None:
            # Average overlapping feathered regions
            x_feather_buffer = torch.where(x_feather_count > 1, x_feather_buffer / x_feather_count, x_feather_buffer)
            x_feather_mask   = torch.where(x_feather_count > 1, x_feather_mask   / x_feather_count, x_feather_mask)
            # Weighted average with original x_buffer
            x_out = torch.where(x_feather_count > 0, x_out * (1 - x_feather_mask) + x_feather_buffer * x_feather_mask, x_out)

        return x_out

    def get_noise(self, x_in:Tensor, sigma_in:Tensor, cond_in:Dict[str, Tensor], step:int) -> Tensor:
        # NOTE: The following code is analytically wrong but aesthetically beautiful
        cond_in_original = cond_in.copy()

        def org_func(x:Tensor):
            return shared.sd_model.apply_model(x, sigma_in, cond=cond_in_original)

        def repeat_func(x_tile:Tensor, bboxes:List[CustomBBox]):
            sigma_in_tile = sigma_in.repeat(len(bboxes))
            cond_out = self.repeat_cond_dict(cond_in_original, bboxes)
            x_tile_out = shared.sd_model.apply_model(x_tile, sigma_in_tile, cond=cond_out)
            return x_tile_out

        def custom_func(x:Tensor, bbox_id:int, bbox:CustomBBox):
            # The negative prompt in custom bbox should not be used for noise inversion
            # otherwise the result will be astonishingly bad.
            tcond = Condition.reconstruct_cond(bbox.cond, step).unsqueeze_(0)
            icond = self.get_icond(cond_in_original)
            if icond.shape[2:] == (self.h, self.w):
                icond = icond[bbox.slicer]
            cond_out = self.make_cond_dict(cond_in, tcond, icond)
            return shared.sd_model.apply_model(x, sigma_in, cond=cond_out)

        return self.sample_one_step(x_in, org_func, repeat_func, custom_func)