Upload tilevae.py

tilevae.py

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+'''
+# ------------------------------------------------------------------------
+#
+#   Tiled VAE
+#
+#   Introducing a revolutionary new optimization designed to make
+#   the VAE work with giant images on limited VRAM!
+#   Say goodbye to the frustration of OOM and hello to seamless output!
+#
+# ------------------------------------------------------------------------
+#
+#   This script is a wild hack that splits the image into tiles,
+#   encodes each tile separately, and merges the result back together.
+#
+#   Advantages:
+#   - The VAE can now work with giant images on limited VRAM
+#       (~10 GB for 8K images!)
+#   - The merged output is completely seamless without any post-processing.
+#
+#   Drawbacks:
+#   - NaNs always appear in for 8k images when you use fp16 (half) VAE
+#       You must use --no-half-vae to disable half VAE for that giant image.
+#   - The gradient calculation is not compatible with this hack. It
+#       will break any backward() or torch.autograd.grad() that passes VAE.
+#       (But you can still use the VAE to generate training data.)
+#
+#   How it works:
+#   1. The image is split into tiles, which are then padded with 11/32 pixels' in the decoder/encoder.
+#   2. When Fast Mode is disabled:
+#       1. The original VAE forward is decomposed into a task queue and a task worker, which starts to process each tile.
+#       2. When GroupNorm is needed, it suspends, stores current GroupNorm mean and var, send everything to RAM, and turns to the next tile.
+#       3. After all GroupNorm means and vars are summarized, it applies group norm to tiles and continues. 
+#       4. A zigzag execution order is used to reduce unnecessary data transfer.
+#   3. When Fast Mode is enabled:
+#       1. The original input is downsampled and passed to a separate task queue.
+#       2. Its group norm parameters are recorded and used by all tiles' task queues.
+#       3. Each tile is separately processed without any RAM-VRAM data transfer.
+#   4. After all tiles are processed, tiles are written to a result buffer and returned.
+#   Encoder color fix = only estimate GroupNorm before downsampling, i.e., run in a semi-fast mode.
+#
+#   Enjoy!
+#
+#   @Author: LI YI @ Nanyang Technological University - Singapore
+#   @Date: 2023-03-02
+#   @License: CC BY-NC-SA 4.0
+#
+#   Please give me a star if you like this project!
+#
+# -------------------------------------------------------------------------
+'''
+
+import gc
+import math
+from time import time
+from tqdm import tqdm
+
+import torch
+import torch.version
+import torch.nn.functional as F
+import gradio as gr
+
+import modules.scripts as scripts
+import modules.devices as devices
+from modules.shared import state
+from modules.ui import gr_show
+from modules.processing import opt_f
+from modules.sd_vae_approx import cheap_approximation
+from ldm.modules.diffusionmodules.model import AttnBlock, MemoryEfficientAttnBlock
+
+from tile_utils.attn import get_attn_func
+from tile_utils.typing import Processing
+
+
+def get_rcmd_enc_tsize():
+    if torch.cuda.is_available() and devices.device not in ['cpu', devices.cpu]:
+        total_memory = torch.cuda.get_device_properties(devices.device).total_memory // 2**20
+        if   total_memory > 16*1000: ENCODER_TILE_SIZE = 3072
+        elif total_memory > 12*1000: ENCODER_TILE_SIZE = 2048
+        elif total_memory >  8*1000: ENCODER_TILE_SIZE = 1536
+        else:                        ENCODER_TILE_SIZE = 960
+    else:                            ENCODER_TILE_SIZE = 512
+    return ENCODER_TILE_SIZE
+
+
+def get_rcmd_dec_tsize():
+    if torch.cuda.is_available() and devices.device not in ['cpu', devices.cpu]:
+        total_memory = torch.cuda.get_device_properties(devices.device).total_memory // 2**20
+        if   total_memory > 30*1000: DECODER_TILE_SIZE = 256
+        elif total_memory > 16*1000: DECODER_TILE_SIZE = 192
+        elif total_memory > 12*1000: DECODER_TILE_SIZE = 128
+        elif total_memory >  8*1000: DECODER_TILE_SIZE = 96
+        else:                        DECODER_TILE_SIZE = 64
+    else:                            DECODER_TILE_SIZE = 64
+    return DECODER_TILE_SIZE
+
+
+def inplace_nonlinearity(x):
+    # Test: fix for Nans
+    return F.silu(x, inplace=True)
+
+
+def attn2task(task_queue, net):
+    attn_forward = get_attn_func()
+    task_queue.append(('store_res', lambda x: x))
+    task_queue.append(('pre_norm', net.norm))
+    task_queue.append(('attn', lambda x, net=net: attn_forward(net, x)))
+    task_queue.append(['add_res', None])
+
+
+def resblock2task(queue, block):
+    """
+    Turn a ResNetBlock into a sequence of tasks and append to the task queue
+
+    @param queue: the target task queue
+    @param block: ResNetBlock
+
+    """
+    if block.in_channels != block.out_channels:
+        if block.use_conv_shortcut:
+            queue.append(('store_res', block.conv_shortcut))
+        else:
+            queue.append(('store_res', block.nin_shortcut))
+    else:
+        queue.append(('store_res', lambda x: x))
+    queue.append(('pre_norm', block.norm1))
+    queue.append(('silu', inplace_nonlinearity))
+    queue.append(('conv1', block.conv1))
+    queue.append(('pre_norm', block.norm2))
+    queue.append(('silu', inplace_nonlinearity))
+    queue.append(('conv2', block.conv2))
+    queue.append(['add_res', None])
+
+
+def build_sampling(task_queue, net, is_decoder):
+    """
+    Build the sampling part of a task queue
+    @param task_queue: the target task queue
+    @param net: the network
+    @param is_decoder: currently building decoder or encoder
+    """
+    if is_decoder:
+        resblock2task(task_queue, net.mid.block_1)
+        attn2task(task_queue, net.mid.attn_1)
+        resblock2task(task_queue, net.mid.block_2)
+        resolution_iter = reversed(range(net.num_resolutions))
+        block_ids = net.num_res_blocks + 1
+        condition = 0
+        module = net.up
+        func_name = 'upsample'
+    else:
+        resolution_iter = range(net.num_resolutions)
+        block_ids = net.num_res_blocks
+        condition = net.num_resolutions - 1
+        module = net.down
+        func_name = 'downsample'
+
+    for i_level in resolution_iter:
+        for i_block in range(block_ids):
+            resblock2task(task_queue, module[i_level].block[i_block])
+        if i_level != condition:
+            task_queue.append((func_name, getattr(module[i_level], func_name)))
+
+    if not is_decoder:
+        resblock2task(task_queue, net.mid.block_1)
+        attn2task(task_queue, net.mid.attn_1)
+        resblock2task(task_queue, net.mid.block_2)
+
+
+def build_task_queue(net, is_decoder):
+    """
+    Build a single task queue for the encoder or decoder
+    @param net: the VAE decoder or encoder network
+    @param is_decoder: currently building decoder or encoder
+    @return: the task queue
+    """
+    task_queue = []
+    task_queue.append(('conv_in', net.conv_in))
+
+    # construct the sampling part of the task queue
+    # because encoder and decoder share the same architecture, we extract the sampling part
+    build_sampling(task_queue, net, is_decoder)
+
+    if not is_decoder or not net.give_pre_end:
+        task_queue.append(('pre_norm', net.norm_out))
+        task_queue.append(('silu', inplace_nonlinearity))
+        task_queue.append(('conv_out', net.conv_out))
+        if is_decoder and net.tanh_out:
+            task_queue.append(('tanh', torch.tanh))
+
+    return task_queue
+
+
+def clone_task_queue(task_queue):
+    """
+    Clone a task queue
+    @param task_queue: the task queue to be cloned
+    @return: the cloned task queue
+    """
+    return [[item for item in task] for task in task_queue]
+
+
+def get_var_mean(input, num_groups, eps=1e-6):
+    """
+    Get mean and var for group norm
+    """
+    b, c = input.size(0), input.size(1)
+    channel_in_group = int(c/num_groups)
+    input_reshaped = input.contiguous().view(1, int(b * num_groups), channel_in_group, *input.size()[2:])
+    var, mean = torch.var_mean(input_reshaped, dim=[0, 2, 3, 4], unbiased=False)
+    return var, mean
+
+
+def custom_group_norm(input, num_groups, mean, var, weight=None, bias=None, eps=1e-6):
+    """
+    Custom group norm with fixed mean and var
+
+    @param input: input tensor
+    @param num_groups: number of groups. by default, num_groups = 32
+    @param mean: mean, must be pre-calculated by get_var_mean
+    @param var: var, must be pre-calculated by get_var_mean
+    @param weight: weight, should be fetched from the original group norm
+    @param bias: bias, should be fetched from the original group norm
+    @param eps: epsilon, by default, eps = 1e-6 to match the original group norm
+
+    @return: normalized tensor
+    """
+    b, c = input.size(0), input.size(1)
+    channel_in_group = int(c/num_groups)
+    input_reshaped = input.contiguous().view(
+        1, int(b * num_groups), channel_in_group, *input.size()[2:])
+
+    out = F.batch_norm(input_reshaped, mean, var, weight=None, bias=None, training=False, momentum=0, eps=eps)
+    out = out.view(b, c, *input.size()[2:])
+
+    # post affine transform
+    if weight is not None:
+        out *= weight.view(1, -1, 1, 1)
+    if bias is not None:
+        out += bias.view(1, -1, 1, 1)
+    return out
+
+
+def crop_valid_region(x, input_bbox, target_bbox, is_decoder):
+    """
+    Crop the valid region from the tile
+    @param x: input tile
+    @param input_bbox: original input bounding box
+    @param target_bbox: output bounding box
+    @param scale: scale factor
+    @return: cropped tile
+    """
+    padded_bbox = [i * 8 if is_decoder else i//8 for i in input_bbox]
+    margin = [target_bbox[i] - padded_bbox[i] for i in range(4)]
+    return x[:, :, margin[2]:x.size(2)+margin[3], margin[0]:x.size(3)+margin[1]]
+
+
+# ↓↓↓ https://github.com/Kahsolt/stable-diffusion-webui-vae-tile-infer ↓↓↓
+
+def perfcount(fn):
+    def wrapper(*args, **kwargs):
+        ts = time()
+
+        if torch.cuda.is_available():
+            torch.cuda.reset_peak_memory_stats(devices.device)
+        devices.torch_gc()
+        gc.collect()
+
+        ret = fn(*args, **kwargs)
+
+        devices.torch_gc()
+        gc.collect()
+        if torch.cuda.is_available():
+            vram = torch.cuda.max_memory_allocated(devices.device) / 2**20
+            print(f'[Tiled VAE]: Done in {time() - ts:.3f}s, max VRAM alloc {vram:.3f} MB')
+        else:
+            print(f'[Tiled VAE]: Done in {time() - ts:.3f}s')
+
+        return ret
+    return wrapper
+
+# ↑↑↑ https://github.com/Kahsolt/stable-diffusion-webui-vae-tile-infer ↑↑↑
+
+
+class GroupNormParam:
+
+    def __init__(self):
+        self.var_list = []
+        self.mean_list = []
+        self.pixel_list = []
+        self.weight = None
+        self.bias = None
+
+    def add_tile(self, tile, layer):
+        var, mean = get_var_mean(tile, 32)
+        # For giant images, the variance can be larger than max float16
+        # In this case we create a copy to float32
+        if var.dtype == torch.float16 and var.isinf().any():
+            fp32_tile = tile.float()
+            var, mean = get_var_mean(fp32_tile, 32)
+        # ============= DEBUG: test for infinite =============
+        # if torch.isinf(var).any():
+        #    print('var: ', var)
+        # ====================================================
+        self.var_list.append(var)
+        self.mean_list.append(mean)
+        self.pixel_list.append(
+            tile.shape[2]*tile.shape[3])
+        if hasattr(layer, 'weight'):
+            self.weight = layer.weight
+            self.bias = layer.bias
+        else:
+            self.weight = None
+            self.bias = None
+
+    def summary(self):
+        """
+        summarize the mean and var and return a function
+        that apply group norm on each tile
+        """
+        if len(self.var_list) == 0: return None
+
+        var = torch.vstack(self.var_list)
+        mean = torch.vstack(self.mean_list)
+        max_value = max(self.pixel_list)
+        pixels = torch.tensor(self.pixel_list, dtype=torch.float32, device=devices.device) / max_value
+        sum_pixels = torch.sum(pixels)
+        pixels = pixels.unsqueeze(1) / sum_pixels
+        var = torch.sum(var * pixels, dim=0)
+        mean = torch.sum(mean * pixels, dim=0)
+        return lambda x:  custom_group_norm(x, 32, mean, var, self.weight, self.bias)
+
+    @staticmethod
+    def from_tile(tile, norm):
+        """
+        create a function from a single tile without summary
+        """
+        var, mean = get_var_mean(tile, 32)
+        if var.dtype == torch.float16 and var.isinf().any():
+            fp32_tile = tile.float()
+            var, mean = get_var_mean(fp32_tile, 32)
+            # if it is a macbook, we need to convert back to float16
+            if var.device.type == 'mps':
+                # clamp to avoid overflow
+                var = torch.clamp(var, 0, 60000)
+                var = var.half()
+                mean = mean.half()
+        if hasattr(norm, 'weight'):
+            weight = norm.weight
+            bias = norm.bias
+        else:
+            weight = None
+            bias = None
+
+        def group_norm_func(x, mean=mean, var=var, weight=weight, bias=bias):
+            return custom_group_norm(x, 32, mean, var, weight, bias, 1e-6)
+        return group_norm_func
+
+
+class VAEHook:
+
+    def __init__(self, net, tile_size, is_decoder:bool, fast_decoder:bool, fast_encoder:bool, color_fix:bool, to_gpu:bool=False):
+        self.net = net                  # encoder | decoder
+        self.tile_size = tile_size
+        self.is_decoder = is_decoder
+        self.fast_mode = (fast_encoder and not is_decoder) or (fast_decoder and is_decoder)
+        self.color_fix = color_fix and not is_decoder
+        self.to_gpu = to_gpu
+        self.pad = 11 if is_decoder else 32         # FIXME: magic number
+
+    def __call__(self, x):
+        original_device = next(self.net.parameters()).device
+        try:
+            if self.to_gpu:
+                self.net = self.net.to(devices.get_optimal_device())
+        
+            B, C, H, W = x.shape
+            if max(H, W) <= self.pad * 2 + self.tile_size:
+                print("[Tiled VAE]: the input size is tiny and unnecessary to tile.")
+                return self.net.original_forward(x)
+            else:
+                return self.vae_tile_forward(x)
+        finally:
+            self.net = self.net.to(original_device)
+
+    def get_best_tile_size(self, lowerbound, upperbound):
+        """
+        Get the best tile size for GPU memory
+        """
+        divider = 32
+        while divider >= 2:
+            remainer = lowerbound % divider
+            if remainer == 0:
+                return lowerbound
+            candidate = lowerbound - remainer + divider
+            if candidate <= upperbound:
+                return candidate
+            divider //= 2
+        return lowerbound
+
+    def split_tiles(self, h, w):
+        """
+        Tool function to split the image into tiles
+        @param h: height of the image
+        @param w: width of the image
+        @return: tile_input_bboxes, tile_output_bboxes
+        """
+        tile_input_bboxes, tile_output_bboxes = [], []
+        tile_size = self.tile_size
+        pad = self.pad
+        num_height_tiles = math.ceil((h - 2 * pad) / tile_size)
+        num_width_tiles = math.ceil((w - 2 * pad) / tile_size)
+        # If any of the numbers are 0, we let it be 1
+        # This is to deal with long and thin images
+        num_height_tiles = max(num_height_tiles, 1)
+        num_width_tiles = max(num_width_tiles, 1)
+
+        # Suggestions from https://github.com/Kahsolt: auto shrink the tile size
+        real_tile_height = math.ceil((h - 2 * pad) / num_height_tiles)
+        real_tile_width = math.ceil((w - 2 * pad) / num_width_tiles)
+        real_tile_height = self.get_best_tile_size(real_tile_height, tile_size)
+        real_tile_width = self.get_best_tile_size(real_tile_width, tile_size)
+
+        print(f'[Tiled VAE]: split to {num_height_tiles}x{num_width_tiles} = {num_height_tiles*num_width_tiles} tiles. ' +
+              f'Optimal tile size {real_tile_width}x{real_tile_height}, original tile size {tile_size}x{tile_size}')
+
+        for i in range(num_height_tiles):
+            for j in range(num_width_tiles):
+                # bbox: [x1, x2, y1, y2]
+                # the padding is is unnessary for image borders. So we directly start from (32, 32)
+                input_bbox = [
+                    pad + j * real_tile_width,
+                    min(pad + (j + 1) * real_tile_width, w),
+                    pad + i * real_tile_height,
+                    min(pad + (i + 1) * real_tile_height, h),
+                ]
+
+                # if the output bbox is close to the image boundary, we extend it to the image boundary
+                output_bbox = [
+                    input_bbox[0] if input_bbox[0] > pad else 0,
+                    input_bbox[1] if input_bbox[1] < w - pad else w,
+                    input_bbox[2] if input_bbox[2] > pad else 0,
+                    input_bbox[3] if input_bbox[3] < h - pad else h,
+                ]
+
+                # scale to get the final output bbox
+                output_bbox = [x * 8 if self.is_decoder else x // 8 for x in output_bbox]
+                tile_output_bboxes.append(output_bbox)
+
+                # indistinguishable expand the input bbox by pad pixels
+                tile_input_bboxes.append([
+                    max(0, input_bbox[0] - pad),
+                    min(w, input_bbox[1] + pad),
+                    max(0, input_bbox[2] - pad),
+                    min(h, input_bbox[3] + pad),
+                ])
+
+        return tile_input_bboxes, tile_output_bboxes
+
+    @torch.no_grad()
+    def estimate_group_norm(self, z, task_queue, color_fix):
+        device = z.device
+        tile = z
+        last_id = len(task_queue) - 1
+        while last_id >= 0 and task_queue[last_id][0] != 'pre_norm':
+            last_id -= 1
+        if last_id <= 0 or task_queue[last_id][0] != 'pre_norm':
+            raise ValueError('No group norm found in the task queue')
+        # estimate until the last group norm
+        for i in range(last_id + 1):
+            task = task_queue[i]
+            if task[0] == 'pre_norm':
+                group_norm_func = GroupNormParam.from_tile(tile, task[1])
+                task_queue[i] = ('apply_norm', group_norm_func)
+                if i == last_id:
+                    return True
+                tile = group_norm_func(tile)
+            elif task[0] == 'store_res':
+                task_id = i + 1
+                while task_id < last_id and task_queue[task_id][0] != 'add_res':
+                    task_id += 1
+                if task_id >= last_id:
+                    continue
+                task_queue[task_id][1] = task[1](tile)
+            elif task[0] == 'add_res':
+                tile += task[1].to(device)
+                task[1] = None
+            elif color_fix and task[0] == 'downsample':
+                for j in range(i, last_id + 1):
+                    if task_queue[j][0] == 'store_res':
+                        task_queue[j] = ('store_res_cpu', task_queue[j][1])
+                return True
+            else:
+                tile = task[1](tile)
+            try:
+                devices.test_for_nans(tile, "vae")
+            except:
+                print(f'Nan detected in fast mode estimation. Fast mode disabled.')
+                return False
+
+        raise IndexError('Should not reach here')
+
+    @perfcount
+    @torch.no_grad()
+    def vae_tile_forward(self, z):
+        """
+        Decode a latent vector z into an image in a tiled manner.
+        @param z: latent vector
+        @return: image
+        """
+        device = next(self.net.parameters()).device
+        net = self.net
+        tile_size = self.tile_size
+        is_decoder = self.is_decoder
+
+        z = z.detach() # detach the input to avoid backprop
+
+        N, height, width = z.shape[0], z.shape[2], z.shape[3]
+        net.last_z_shape = z.shape
+
+        # Split the input into tiles and build a task queue for each tile
+        print(f'[Tiled VAE]: input_size: {z.shape}, tile_size: {tile_size}, padding: {self.pad}')
+
+        in_bboxes, out_bboxes = self.split_tiles(height, width)
+
+        # Prepare tiles by split the input latents
+        tiles = []
+        for input_bbox in in_bboxes:
+            tile = z[:, :, input_bbox[2]:input_bbox[3], input_bbox[0]:input_bbox[1]].cpu()
+            tiles.append(tile)
+
+        num_tiles = len(tiles)
+        num_completed = 0
+
+        # Build task queues
+        single_task_queue = build_task_queue(net, is_decoder)
+        if self.fast_mode:
+            # Fast mode: downsample the input image to the tile size,
+            # then estimate the group norm parameters on the downsampled image
+            scale_factor = tile_size / max(height, width)
+            z = z.to(device)
+            downsampled_z = F.interpolate(z, scale_factor=scale_factor, mode='nearest-exact')
+            # use nearest-exact to keep statictics as close as possible
+            print(f'[Tiled VAE]: Fast mode enabled, estimating group norm parameters on {downsampled_z.shape[3]} x {downsampled_z.shape[2]} image')
+
+            # ======= Special thanks to @Kahsolt for distribution shift issue ======= #
+            # The downsampling will heavily distort its mean and std, so we need to recover it.
+            std_old, mean_old = torch.std_mean(z, dim=[0, 2, 3], keepdim=True)
+            std_new, mean_new = torch.std_mean(downsampled_z, dim=[0, 2, 3], keepdim=True)
+            downsampled_z = (downsampled_z - mean_new) / std_new * std_old + mean_old
+            del std_old, mean_old, std_new, mean_new
+            # occasionally the std_new is too small or too large, which exceeds the range of float16
+            # so we need to clamp it to max z's range.
+            downsampled_z = torch.clamp_(downsampled_z, min=z.min(), max=z.max())
+            estimate_task_queue = clone_task_queue(single_task_queue)
+            if self.estimate_group_norm(downsampled_z, estimate_task_queue, color_fix=self.color_fix):
+                single_task_queue = estimate_task_queue
+            del downsampled_z
+
+        task_queues = [clone_task_queue(single_task_queue) for _ in range(num_tiles)]
+
+        # Dummy result
+        result = None
+        result_approx = None
+        try:
+            with devices.autocast():
+                result_approx = torch.cat([F.interpolate(cheap_approximation(x).unsqueeze(0), scale_factor=opt_f, mode='nearest-exact') for x in z], dim=0).cpu()
+        except: pass
+        # Free memory of input latent tensor
+        del z
+
+        # Task queue execution
+        pbar = tqdm(total=num_tiles * len(task_queues[0]), desc=f"[Tiled VAE]: Executing {'Decoder' if is_decoder else 'Encoder'} Task Queue: ")
+
+        # execute the task back and forth when switch tiles so that we always
+        # keep one tile on the GPU to reduce unnecessary data transfer
+        forward = True
+        interrupted = False
+        #state.interrupted = interrupted
+        while True:
+            if state.interrupted: interrupted = True ; break
+
+            group_norm_param = GroupNormParam()
+            for i in range(num_tiles) if forward else reversed(range(num_tiles)):
+                if state.interrupted: interrupted = True ; break
+
+                tile = tiles[i].to(device)
+                input_bbox = in_bboxes[i]
+                task_queue = task_queues[i]
+
+                interrupted = False
+                while len(task_queue) > 0:
+                    if state.interrupted: interrupted = True ; break
+
+                    # DEBUG: current task
+                    # print('Running task: ', task_queue[0][0], ' on tile ', i, '/', num_tiles, ' with shape ', tile.shape)
+                    task = task_queue.pop(0)
+                    if task[0] == 'pre_norm':
+                        group_norm_param.add_tile(tile, task[1])
+                        break
+                    elif task[0] == 'store_res' or task[0] == 'store_res_cpu':
+                        task_id = 0
+                        res = task[1](tile)
+                        if not self.fast_mode or task[0] == 'store_res_cpu':
+                            res = res.cpu()
+                        while task_queue[task_id][0] != 'add_res':
+                            task_id += 1
+                        task_queue[task_id][1] = res
+                    elif task[0] == 'add_res':
+                        tile += task[1].to(device)
+                        task[1] = None
+                    else:
+                        tile = task[1](tile)
+                    pbar.update(1)
+
+                if interrupted: break
+
+                # check for NaNs in the tile.
+                # If there are NaNs, we abort the process to save user's time
+                devices.test_for_nans(tile, "vae")
+
+                if len(task_queue) == 0:
+                    tiles[i] = None
+                    num_completed += 1
+                    if result is None:      # NOTE: dim C varies from different cases, can only be inited dynamically
+                        result = torch.zeros((N, tile.shape[1], height * 8 if is_decoder else height // 8, width * 8 if is_decoder else width // 8), device=device, requires_grad=False)
+                    result[:, :, out_bboxes[i][2]:out_bboxes[i][3], out_bboxes[i][0]:out_bboxes[i][1]] = crop_valid_region(tile, in_bboxes[i], out_bboxes[i], is_decoder)
+                    del tile
+                elif i == num_tiles - 1 and forward:
+                    forward = False
+                    tiles[i] = tile
+                elif i == 0 and not forward:
+                    forward = True
+                    tiles[i] = tile
+                else:
+                    tiles[i] = tile.cpu()
+                    del tile
+
+            if interrupted: break
+            if num_completed == num_tiles: break
+
+            # insert the group norm task to the head of each task queue
+            group_norm_func = group_norm_param.summary()
+            if group_norm_func is not None:
+                for i in range(num_tiles):
+                    task_queue = task_queues[i]
+                    task_queue.insert(0, ('apply_norm', group_norm_func))
+
+        # Done!
+        pbar.close()
+        return result if result is not None else result_approx.to(device)
+
+
+class Script(scripts.Script):
+
+    def __init__(self):
+        self.hooked = False
+
+    def title(self):
+        return "Tiled VAE"
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def ui(self, is_img2img):
+        tab = 't2i' if not is_img2img else 'i2i'
+        uid = lambda name: f'MD-{tab}-{name}'
+
+        with gr.Accordion('Tiled VAE', open=False, elem_id=f'MDV-{tab}'):
+            with gr.Row() as tab_enable:
+                enabled = gr.Checkbox(label='Enable Tiled VAE', value=False, elem_id=uid('enable'))
+                vae_to_gpu = gr.Checkbox(label='Move VAE to GPU (if possible)', value=True, elem_id=uid('vae2gpu'))
+
+            gr.HTML('<p style="margin-bottom:0.8em"> Recommended to set tile sizes as large as possible before got CUDA error: out of memory. </p>')
+            with gr.Row() as tab_size:
+                encoder_tile_size = gr.Slider(label='Encoder Tile Size', minimum=256, maximum=4096, step=16, value=get_rcmd_enc_tsize(), elem_id=uid('enc-size'))
+                decoder_tile_size = gr.Slider(label='Decoder Tile Size', minimum=48,  maximum=512,  step=16, value=get_rcmd_dec_tsize(), elem_id=uid('dec-size'))
+                reset = gr.Button(value='↻ Reset', variant='tool')
+                reset.click(fn=lambda: [get_rcmd_enc_tsize(), get_rcmd_dec_tsize()], outputs=[encoder_tile_size, decoder_tile_size], show_progress=False)
+
+            with gr.Row() as tab_param:
+                fast_encoder = gr.Checkbox(label='Fast Encoder', value=True, elem_id=uid('fastenc'))
+                color_fix    = gr.Checkbox(label='Fast Encoder Color Fix', value=False, visible=True, elem_id=uid('fastenc-colorfix'))
+                fast_decoder = gr.Checkbox(label='Fast Decoder', value=True, elem_id=uid('fastdec'))
+
+                fast_encoder.change(fn=gr_show, inputs=fast_encoder, outputs=color_fix, show_progress=False)
+
+        return [
+            enabled, 
+            encoder_tile_size, decoder_tile_size,
+            vae_to_gpu, fast_decoder, fast_encoder, color_fix, 
+        ]
+
+    def process(self, p:Processing, 
+            enabled:bool, 
+            encoder_tile_size:int, decoder_tile_size:int, 
+            vae_to_gpu:bool, fast_decoder:bool, fast_encoder:bool, color_fix:bool
+        ):
+        enabled = True
+        encoder_tile_size = 1536
+        decoder_tile_size = 96
+        vae_to_gpu = True
+        fast_decoder = True
+        fast_encoder = False
+        color_fix = False
+        # for shorthand
+        vae = p.sd_model.first_stage_model
+        encoder = vae.encoder
+        decoder = vae.decoder
+
+        # undo hijack if disabled (in cases last time crashed)
+        if not enabled:
+            if self.hooked:
+                if isinstance(encoder.forward, VAEHook):
+                    encoder.forward.net = None
+                    encoder.forward = encoder.original_forward
+                if isinstance(decoder.forward, VAEHook):
+                    decoder.forward.net = None
+                    decoder.forward = decoder.original_forward
+                self.hooked = False
+            return
+
+        if devices.get_optimal_device_name().startswith('cuda') and vae.device == devices.cpu and not vae_to_gpu:
+            print("[Tiled VAE] warn: VAE is not on GPU, check 'Move VAE to GPU' if possible.")
+
+        # do hijack
+        kwargs = {
+            'fast_decoder': fast_decoder, 
+            'fast_encoder': fast_encoder, 
+            'color_fix':    color_fix, 
+            'to_gpu':       vae_to_gpu,
+        }
+
+        # save original forward (only once)
+        if not hasattr(encoder, 'original_forward'): setattr(encoder, 'original_forward', encoder.forward)
+        if not hasattr(decoder, 'original_forward'): setattr(decoder, 'original_forward', decoder.forward)
+
+        self.hooked = True
+        
+        encoder.forward = VAEHook(encoder, encoder_tile_size, is_decoder=False, **kwargs)
+        decoder.forward = VAEHook(decoder, decoder_tile_size, is_decoder=True,  **kwargs)
+
+    def postprocess(self, p:Processing, processed, enabled:bool, *args):
+        if not enabled: return
+
+        vae = p.sd_model.first_stage_model
+        encoder = vae.encoder
+        decoder = vae.decoder
+        if isinstance(encoder.forward, VAEHook):
+            encoder.forward.net = None
+            encoder.forward = encoder.original_forward
+        if isinstance(decoder.forward, VAEHook):
+            decoder.forward.net = None
+            decoder.forward = decoder.original_forward