Merge branch 'hypertile-in-sample' into dev

3 files changed, 13 insertions, 403 deletions

diff --git a/modules/hypertile.py b/modules/hypertile.py
deleted file mode 100644
index be898fce..00000000
--- a/modules/hypertile.py
+++ /dev/null
@@ -1,371 +0,0 @@
-"""
-Hypertile module for splitting attention layers in SD-1.5 U-Net and SD-1.5 VAE
-Warn : The patch works well only if the input image has a width and height that are multiples of 128
-Author : @tfernd Github : https://github.com/tfernd/HyperTile
-"""
-
-from __future__ import annotations
-from typing import Callable
-from typing_extensions import Literal
-
-import logging
-from functools import wraps, cache
-from contextlib import contextmanager
-
-import math
-import torch.nn as nn
-import random
-
-from einops import rearrange
-
-# TODO add SD-XL layers
-DEPTH_LAYERS = {
-    0: [
-        # SD 1.5 U-Net (diffusers)
-        "down_blocks.0.attentions.0.transformer_blocks.0.attn1",
-        "down_blocks.0.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.3.attentions.0.transformer_blocks.0.attn1",
-        "up_blocks.3.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.3.attentions.2.transformer_blocks.0.attn1",
-        # SD 1.5 U-Net (ldm)
-        "input_blocks.1.1.transformer_blocks.0.attn1",
-        "input_blocks.2.1.transformer_blocks.0.attn1",
-        "output_blocks.9.1.transformer_blocks.0.attn1",
-        "output_blocks.10.1.transformer_blocks.0.attn1",
-        "output_blocks.11.1.transformer_blocks.0.attn1",
-        # SD 1.5 VAE
-        "decoder.mid_block.attentions.0",
-        "decoder.mid.attn_1",
-    ],
-    1: [
-        # SD 1.5 U-Net (diffusers)
-        "down_blocks.1.attentions.0.transformer_blocks.0.attn1",
-        "down_blocks.1.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.2.attentions.0.transformer_blocks.0.attn1",
-        "up_blocks.2.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.2.attentions.2.transformer_blocks.0.attn1",
-        # SD 1.5 U-Net (ldm)
-        "input_blocks.4.1.transformer_blocks.0.attn1",
-        "input_blocks.5.1.transformer_blocks.0.attn1",
-        "output_blocks.6.1.transformer_blocks.0.attn1",
-        "output_blocks.7.1.transformer_blocks.0.attn1",
-        "output_blocks.8.1.transformer_blocks.0.attn1",
-    ],
-    2: [
-        # SD 1.5 U-Net (diffusers)
-        "down_blocks.2.attentions.0.transformer_blocks.0.attn1",
-        "down_blocks.2.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.1.attentions.0.transformer_blocks.0.attn1",
-        "up_blocks.1.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.1.attentions.2.transformer_blocks.0.attn1",
-        # SD 1.5 U-Net (ldm)
-        "input_blocks.7.1.transformer_blocks.0.attn1",
-        "input_blocks.8.1.transformer_blocks.0.attn1",
-        "output_blocks.3.1.transformer_blocks.0.attn1",
-        "output_blocks.4.1.transformer_blocks.0.attn1",
-        "output_blocks.5.1.transformer_blocks.0.attn1",
-    ],
-    3: [
-        # SD 1.5 U-Net (diffusers)
-        "mid_block.attentions.0.transformer_blocks.0.attn1",
-        # SD 1.5 U-Net (ldm)
-        "middle_block.1.transformer_blocks.0.attn1",
-    ],
-}
-# XL layers, thanks for GitHub@gel-crabs for the help
-DEPTH_LAYERS_XL = {
-    0: [
-        # SD 1.5 U-Net (diffusers)
-        "down_blocks.0.attentions.0.transformer_blocks.0.attn1",
-        "down_blocks.0.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.3.attentions.0.transformer_blocks.0.attn1",
-        "up_blocks.3.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.3.attentions.2.transformer_blocks.0.attn1",
-        # SD 1.5 U-Net (ldm)
-        "input_blocks.4.1.transformer_blocks.0.attn1",
-        "input_blocks.5.1.transformer_blocks.0.attn1",
-        "output_blocks.3.1.transformer_blocks.0.attn1",
-        "output_blocks.4.1.transformer_blocks.0.attn1",
-        "output_blocks.5.1.transformer_blocks.0.attn1",
-        # SD 1.5 VAE
-        "decoder.mid_block.attentions.0",
-        "decoder.mid.attn_1",
-    ],
-    1: [
-        # SD 1.5 U-Net (diffusers)
-        #"down_blocks.1.attentions.0.transformer_blocks.0.attn1",
-        #"down_blocks.1.attentions.1.transformer_blocks.0.attn1",
-        #"up_blocks.2.attentions.0.transformer_blocks.0.attn1",
-        #"up_blocks.2.attentions.1.transformer_blocks.0.attn1",
-        #"up_blocks.2.attentions.2.transformer_blocks.0.attn1",
-        # SD 1.5 U-Net (ldm)
-        "input_blocks.4.1.transformer_blocks.1.attn1",
-        "input_blocks.5.1.transformer_blocks.1.attn1",
-        "output_blocks.3.1.transformer_blocks.1.attn1",
-        "output_blocks.4.1.transformer_blocks.1.attn1",
-        "output_blocks.5.1.transformer_blocks.1.attn1",
-        "input_blocks.7.1.transformer_blocks.0.attn1",
-        "input_blocks.8.1.transformer_blocks.0.attn1",
-        "output_blocks.0.1.transformer_blocks.0.attn1",
-        "output_blocks.1.1.transformer_blocks.0.attn1",
-        "output_blocks.2.1.transformer_blocks.0.attn1",
-        "input_blocks.7.1.transformer_blocks.1.attn1",
-        "input_blocks.8.1.transformer_blocks.1.attn1",
-        "output_blocks.0.1.transformer_blocks.1.attn1",
-        "output_blocks.1.1.transformer_blocks.1.attn1",
-        "output_blocks.2.1.transformer_blocks.1.attn1",
-        "input_blocks.7.1.transformer_blocks.2.attn1",
-        "input_blocks.8.1.transformer_blocks.2.attn1",
-        "output_blocks.0.1.transformer_blocks.2.attn1",
-        "output_blocks.1.1.transformer_blocks.2.attn1",
-        "output_blocks.2.1.transformer_blocks.2.attn1",
-        "input_blocks.7.1.transformer_blocks.3.attn1",
-        "input_blocks.8.1.transformer_blocks.3.attn1",
-        "output_blocks.0.1.transformer_blocks.3.attn1",
-        "output_blocks.1.1.transformer_blocks.3.attn1",
-        "output_blocks.2.1.transformer_blocks.3.attn1",
-        "input_blocks.7.1.transformer_blocks.4.attn1",
-        "input_blocks.8.1.transformer_blocks.4.attn1",
-        "output_blocks.0.1.transformer_blocks.4.attn1",
-        "output_blocks.1.1.transformer_blocks.4.attn1",
-        "output_blocks.2.1.transformer_blocks.4.attn1",
-        "input_blocks.7.1.transformer_blocks.5.attn1",
-        "input_blocks.8.1.transformer_blocks.5.attn1",
-        "output_blocks.0.1.transformer_blocks.5.attn1",
-        "output_blocks.1.1.transformer_blocks.5.attn1",
-        "output_blocks.2.1.transformer_blocks.5.attn1",
-        "input_blocks.7.1.transformer_blocks.6.attn1",
-        "input_blocks.8.1.transformer_blocks.6.attn1",
-        "output_blocks.0.1.transformer_blocks.6.attn1",
-        "output_blocks.1.1.transformer_blocks.6.attn1",
-        "output_blocks.2.1.transformer_blocks.6.attn1",
-        "input_blocks.7.1.transformer_blocks.7.attn1",
-        "input_blocks.8.1.transformer_blocks.7.attn1",
-        "output_blocks.0.1.transformer_blocks.7.attn1",
-        "output_blocks.1.1.transformer_blocks.7.attn1",
-        "output_blocks.2.1.transformer_blocks.7.attn1",
-        "input_blocks.7.1.transformer_blocks.8.attn1",
-        "input_blocks.8.1.transformer_blocks.8.attn1",
-        "output_blocks.0.1.transformer_blocks.8.attn1",
-        "output_blocks.1.1.transformer_blocks.8.attn1",
-        "output_blocks.2.1.transformer_blocks.8.attn1",
-        "input_blocks.7.1.transformer_blocks.9.attn1",
-        "input_blocks.8.1.transformer_blocks.9.attn1",
-        "output_blocks.0.1.transformer_blocks.9.attn1",
-        "output_blocks.1.1.transformer_blocks.9.attn1",
-        "output_blocks.2.1.transformer_blocks.9.attn1",
-    ],
-    2: [
-        # SD 1.5 U-Net (diffusers)
-        "mid_block.attentions.0.transformer_blocks.0.attn1",
-        # SD 1.5 U-Net (ldm)
-        "middle_block.1.transformer_blocks.0.attn1",
-        "middle_block.1.transformer_blocks.1.attn1",
-        "middle_block.1.transformer_blocks.2.attn1",
-        "middle_block.1.transformer_blocks.3.attn1",
-        "middle_block.1.transformer_blocks.4.attn1",
-        "middle_block.1.transformer_blocks.5.attn1",
-        "middle_block.1.transformer_blocks.6.attn1",
-        "middle_block.1.transformer_blocks.7.attn1",
-        "middle_block.1.transformer_blocks.8.attn1",
-        "middle_block.1.transformer_blocks.9.attn1",
-    ],
-    3 : [] # TODO - separate layers for SD-XL
-}
-
-
-RNG_INSTANCE = random.Random()
-
-def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
-    """
-    Returns a random divisor of value that
-        x * min_value <= value
-    if max_options is 1, the behavior is deterministic
-    """
-    min_value = min(min_value, value)
-
-    # All big divisors of value (inclusive)
-    divisors = [i for i in range(min_value, value + 1) if value % i == 0] # divisors in small -> big order
-
-    ns = [value // i for i in divisors[:max_options]]  # has at least 1 element # big -> small order
-
-    idx = RNG_INSTANCE.randint(0, len(ns) - 1)
-
-    return ns[idx]
-
-def set_hypertile_seed(seed: int) -> None:
-    RNG_INSTANCE.seed(seed)
-
-def largest_tile_size_available(width:int, height:int) -> int:
-    """
-    Calculates the largest tile size available for a given width and height
-    Tile size is always a power of 2
-    """
-    gcd = math.gcd(width, height)
-    largest_tile_size_available = 1
-    while gcd % (largest_tile_size_available * 2) == 0:
-        largest_tile_size_available *= 2
-    return largest_tile_size_available
-
-def iterative_closest_divisors(hw:int, aspect_ratio:float) -> tuple[int, int]:
-    """
-    Finds h and w such that h*w = hw and h/w = aspect_ratio
-    We check all possible divisors of hw and return the closest to the aspect ratio
-    """
-    divisors = [i for i in range(2, hw + 1) if hw % i == 0] # all divisors of hw
-    pairs = [(i, hw // i) for i in divisors] # all pairs of divisors of hw
-    ratios = [w/h for h, w in pairs] # all ratios of pairs of divisors of hw
-    closest_ratio = min(ratios, key=lambda x: abs(x - aspect_ratio)) # closest ratio to aspect_ratio
-    closest_pair = pairs[ratios.index(closest_ratio)] # closest pair of divisors to aspect_ratio
-    return closest_pair
-
-@cache
-def find_hw_candidates(hw:int, aspect_ratio:float) -> tuple[int, int]:
-    """
-    Finds h and w such that h*w = hw and h/w = aspect_ratio
-    """
-    h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
-    # find h and w such that h*w = hw and h/w = aspect_ratio
-    if h * w != hw:
-        w_candidate = hw / h
-        # check if w is an integer
-        if not w_candidate.is_integer():
-            h_candidate = hw / w
-            # check if h is an integer
-            if not h_candidate.is_integer():
-                return iterative_closest_divisors(hw, aspect_ratio)
-            else:
-                h = int(h_candidate)
-        else:
-            w = int(w_candidate)
-    return h, w
-
-@contextmanager
-def split_attention(
-    layer: nn.Module,
-    /,
-    aspect_ratio: float,  # width/height
-    tile_size: int = 128,  # 128 for VAE
-    swap_size: int = 1,  # 1 for VAE
-    *,
-    disable: bool = False,
-    max_depth: Literal[0, 1, 2, 3] = 0,  # ! Try 0 or 1
-    scale_depth: bool = True,  # scale the tile-size depending on the depth
-    is_sdxl: bool = False,  # is the model SD-XL
-):
-    # Hijacks AttnBlock from ldm and Attention from diffusers
-
-    if disable:
-        logging.info(f"Attention for {layer.__class__.__qualname__} not splitted")
-        yield
-        return
-
-    latent_tile_size = max(128, tile_size) // 8
-
-    def self_attn_forward(forward: Callable, depth: int, layer_name: str, module: nn.Module) -> Callable:
-        @wraps(forward)
-        def wrapper(*args, **kwargs):
-            x = args[0]
-
-            # VAE
-            if x.ndim == 4:
-                b, c, h, w = x.shape
-
-                nh = random_divisor(h, latent_tile_size, swap_size)
-                nw = random_divisor(w, latent_tile_size, swap_size)
-
-                if nh * nw > 1:
-                    x = rearrange(x, "b c (nh h) (nw w) -> (b nh nw) c h w", nh=nh, nw=nw) # split into nh * nw tiles
-
-                out = forward(x, *args[1:], **kwargs)
-
-                if nh * nw > 1:
-                    out = rearrange(out, "(b nh nw) c h w -> b c (nh h) (nw w)", nh=nh, nw=nw)
-
-            # U-Net
-            else:
-                hw: int = x.size(1)
-                h, w = find_hw_candidates(hw, aspect_ratio)
-                assert h * w == hw, f"Invalid aspect ratio {aspect_ratio} for input of shape {x.shape}, hw={hw}, h={h}, w={w}"
-
-                factor = 2**depth if scale_depth else 1
-                nh = random_divisor(h, latent_tile_size * factor, swap_size)
-                nw = random_divisor(w, latent_tile_size * factor, swap_size)
-
-                module._split_sizes_hypertile.append((nh, nw))  # type: ignore
-
-                if nh * nw > 1:
-                    x = rearrange(x, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)
-
-                out = forward(x, *args[1:], **kwargs)
-
-                if nh * nw > 1:
-                    out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw)
-                    out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw)
-
-            return out
-
-        return wrapper
-
-    # Handle hijacking the forward method and recovering afterwards
-    try:
-        if is_sdxl:
-            layers = DEPTH_LAYERS_XL
-        else:
-            layers = DEPTH_LAYERS
-        for depth in range(max_depth + 1):
-            for layer_name, module in layer.named_modules():
-                if any(layer_name.endswith(try_name) for try_name in layers[depth]):
-                    # print input shape for debugging
-                    logging.debug(f"HyperTile hijacking attention layer at depth {depth}: {layer_name}")
-                    # hijack
-                    module._original_forward_hypertile = module.forward
-                    module.forward = self_attn_forward(module.forward, depth, layer_name, module)
-                    module._split_sizes_hypertile = []
-        yield
-    finally:
-        for layer_name, module in layer.named_modules():
-            # remove hijack
-            if hasattr(module, "_original_forward_hypertile"):
-                if module._split_sizes_hypertile:
-                    logging.debug(f"layer {layer_name} splitted with ({module._split_sizes_hypertile})")
-                # recover
-                module.forward = module._original_forward_hypertile
-                del module._original_forward_hypertile
-                del module._split_sizes_hypertile
-
-def hypertile_context_vae(model:nn.Module, aspect_ratio:float, tile_size:int, opts):
-    """
-    Returns context manager for VAE
-    """
-    enabled = opts.hypertile_split_vae_attn
-    swap_size = opts.hypertile_swap_size_vae
-    max_depth = opts.hypertile_max_depth_vae
-    tile_size_max = opts.hypertile_max_tile_vae
-    return split_attention(
-        model,
-        aspect_ratio=aspect_ratio,
-        tile_size=min(tile_size, tile_size_max),
-        swap_size=swap_size,
-        disable=not enabled,
-        max_depth=max_depth,
-        is_sdxl=False,
-    )
-
-def hypertile_context_unet(model:nn.Module, aspect_ratio:float, tile_size:int, opts, is_sdxl:bool):
-    """
-    Returns context manager for U-Net
-    """
-    enabled = opts.hypertile_split_unet_attn
-    swap_size = opts.hypertile_swap_size_unet
-    max_depth = opts.hypertile_max_depth_unet
-    tile_size_max = opts.hypertile_max_tile_unet
-    return split_attention(
-        model,
-        aspect_ratio=aspect_ratio,
-        tile_size=min(tile_size, tile_size_max),
-        swap_size=swap_size,
-        disable=not enabled,
-        max_depth=max_depth,
-        is_sdxl=is_sdxl,
-    )
diff --git a/modules/processing.py b/modules/processing.py
index 36c2be5e..ac58ef86 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -24,7 +24,6 @@ from modules.shared import opts, cmd_opts, state
 import modules.shared as shared

 import modules.paths as paths

 import modules.face_restoration

-from modules.hypertile import set_hypertile_seed, largest_tile_size_available, hypertile_context_unet, hypertile_context_vae

 import modules.images as images

 import modules.styles

 import modules.sd_models as sd_models

@@ -861,8 +860,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 p.comment(comment)

 

             p.extra_generation_params.update(model_hijack.extra_generation_params)

-            set_hypertile_seed(p.seed)

-            # add batch size + hypertile status to information to reproduce the run

+

             if p.n_iter > 1:

                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"

 

@@ -874,8 +872,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             else:

                 if opts.sd_vae_decode_method != 'Full':

                     p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method

-                with hypertile_context_vae(p.sd_model.first_stage_model, aspect_ratio=p.width / p.height, tile_size=largest_tile_size_available(p.width, p.height), opts=shared.opts):

-                    x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)

+                x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)

 

             x_samples_ddim = torch.stack(x_samples_ddim).float()

             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

@@ -1141,25 +1138,23 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 

     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):

         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)

-        aspect_ratio = self.width / self.height

+

         x = self.rng.next()

-        tile_size = largest_tile_size_available(self.width, self.height)

-        with hypertile_context_vae(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, opts=shared.opts):

-            with hypertile_context_unet(self.sd_model.model, aspect_ratio=aspect_ratio, tile_size=tile_size, is_sdxl=shared.sd_model.is_sdxl, opts=shared.opts):

-                samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))

+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))

         del x

+

         if not self.enable_hr:

             return samples

         devices.torch_gc()

 

         if self.latent_scale_mode is None:

-            with hypertile_context_vae(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, opts=shared.opts):

-                decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)

+            decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)

         else:

             decoded_samples = None

 

         with sd_models.SkipWritingToConfig():

             sd_models.reload_model_weights(info=self.hr_checkpoint_info)

+

         return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)

 

     def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):

@@ -1244,18 +1239,15 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 

         if self.scripts is not None:

             self.scripts.before_hr(self)

-        tile_size = largest_tile_size_available(target_width, target_height)

-        aspect_ratio = self.width / self.height

-        with hypertile_context_vae(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, opts=shared.opts):

-            with hypertile_context_unet(self.sd_model.model, aspect_ratio=aspect_ratio, tile_size=tile_size, is_sdxl=shared.sd_model.is_sdxl, opts=shared.opts):

-                samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)

+

+        samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)

 

         sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())

 

         self.sampler = None

         devices.torch_gc()

-        with hypertile_context_vae(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, opts=shared.opts):

-            decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)

+

+        decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)

 

         self.is_hr_pass = False

         return decoded_samples

@@ -1532,11 +1524,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         if self.initial_noise_multiplier != 1.0:

             self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier

             x *= self.initial_noise_multiplier

-        aspect_ratio = self.width / self.height

-        tile_size = largest_tile_size_available(self.width, self.height)

-        with hypertile_context_vae(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, opts=shared.opts):

-            with hypertile_context_unet(self.sd_model.model, aspect_ratio=aspect_ratio, tile_size=tile_size, is_sdxl=shared.sd_model.is_sdxl, opts=shared.opts):

-                samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)

+

+        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)

 

         if self.mask is not None:

             samples = samples * self.nmask + self.init_latent * self.mask

diff --git a/modules/shared_options.py b/modules/shared_options.py
index 1f860d8c..9bcd7914 100644
--- a/modules/shared_options.py
+++ b/modules/shared_options.py
@@ -201,14 +201,6 @@ options_templates.update(options_section(('optimizations', "Optimizations"), {
     "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length", infotext='Pad conds').info("improves performance when prompt and negative prompt have different lengths; changes seeds"),

     "persistent_cond_cache": OptionInfo(True, "Persistent cond cache").info("do not recalculate conds from prompts if prompts have not changed since previous calculation"),

     "batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond comandline argument"),

-    "hypertile_split_unet_attn" : OptionInfo(False, "Split attention in Unet with HyperTile").link("Github", "https://github.com/tfernd/HyperTile").info("improves performance; changes behavior, but deterministic"),

-    "hypertile_split_vae_attn": OptionInfo(False, "Split attention in VAE with HyperTile").link("Github", "https://github.com/tfernd/HyperTile").info("improves performance; changes behavior, but deterministic"),

-    "hypertile_max_depth_vae" : OptionInfo(3, "Max depth for VAE HyperTile hijack", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}).link("Github", "https://github.com/tfernd/HyperTile"),

-    "hypertile_max_depth_unet" : OptionInfo(3, "Max depth for Unet HyperTile hijack", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}).link("Github", "https://github.com/tfernd/HyperTile"),

-    "hypertile_max_tile_vae" : OptionInfo(128, "Max tile size for VAE HyperTile hijack", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).link("Github", "https://github.com/tfernd/HyperTile"),

-    "hypertile_max_tile_unet" : OptionInfo(256, "Max tile size for Unet HyperTile hijack", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).link("Github", "https://github.com/tfernd/HyperTile"),

-    "hypertile_swap_size_unet": OptionInfo(3, "Swap size for Unet HyperTile hijack", gr.Slider, {"minimum": 0, "maximum": 6, "step": 1}).link("Github", "https://github.com/tfernd/HyperTile"),

-    "hypertile_swap_size_vae": OptionInfo(3, "Swap size for VAE HyperTile hijack", gr.Slider, {"minimum": 0, "maximum": 6, "step": 1}).link("Github", "https://github.com/tfernd/HyperTile"),

 }))

 

 options_templates.update(options_section(('compatibility', "Compatibility"), {