rework hypertile into a built-in extension
This commit is contained in:
AUTOMATIC1111
parent
3a9bf4ac10
commit
d2e0c1ca13
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@ -174,5 +174,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
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- TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
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- LyCORIS - KohakuBlueleaf
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- Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
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- Hypertile - tfernd - https://github.com/tfernd/HyperTile
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- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
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- (You)
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@ -1,10 +1,13 @@
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"""
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Hypertile module for splitting attention layers in SD-1.5 U-Net and SD-1.5 VAE
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Warn : The patch works well only if the input image has a width and height that are multiples of 128
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Author : @tfernd Github : https://github.com/tfernd/HyperTile
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Warn: The patch works well only if the input image has a width and height that are multiples of 128
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Original author: @tfernd Github: https://github.com/tfernd/HyperTile
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"""
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from __future__ import annotations
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import functools
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from dataclasses import dataclass
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from typing import Callable
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from typing_extensions import Literal
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@ -18,6 +21,19 @@ import random
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from einops import rearrange
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@dataclass
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class HypertileParams:
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depth = 0
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layer_name = ""
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tile_size: int = 0
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swap_size: int = 0
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aspect_ratio: float = 1.0
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forward = None
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enabled = False
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# TODO add SD-XL layers
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DEPTH_LAYERS = {
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0: [
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@ -176,6 +192,7 @@ DEPTH_LAYERS_XL = {
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RNG_INSTANCE = random.Random()
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def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
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"""
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Returns a random divisor of value that
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@ -193,10 +210,13 @@ def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
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return ns[idx]
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def set_hypertile_seed(seed: int) -> None:
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RNG_INSTANCE.seed(seed)
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def largest_tile_size_available(width:int, height:int) -> int:
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@functools.cache
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def largest_tile_size_available(width: int, height: int) -> int:
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"""
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Calculates the largest tile size available for a given width and height
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Tile size is always a power of 2
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@ -207,6 +227,7 @@ def largest_tile_size_available(width:int, height:int) -> int:
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largest_tile_size_available *= 2
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return largest_tile_size_available
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def iterative_closest_divisors(hw:int, aspect_ratio:float) -> tuple[int, int]:
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"""
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Finds h and w such that h*w = hw and h/w = aspect_ratio
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@ -219,6 +240,7 @@ def iterative_closest_divisors(hw:int, aspect_ratio:float) -> tuple[int, int]:
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closest_pair = pairs[ratios.index(closest_ratio)] # closest pair of divisors to aspect_ratio
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return closest_pair
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@cache
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def find_hw_candidates(hw:int, aspect_ratio:float) -> tuple[int, int]:
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"""
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@ -240,132 +262,87 @@ def find_hw_candidates(hw:int, aspect_ratio:float) -> tuple[int, int]:
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w = int(w_candidate)
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return h, w
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@contextmanager
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def split_attention(
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layer: nn.Module,
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/,
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aspect_ratio: float, # width/height
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tile_size: int = 128, # 128 for VAE
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swap_size: int = 1, # 1 for VAE
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*,
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disable: bool = False,
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max_depth: Literal[0, 1, 2, 3] = 0, # ! Try 0 or 1
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scale_depth: bool = True, # scale the tile-size depending on the depth
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is_sdxl: bool = False, # is the model SD-XL
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):
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# Hijacks AttnBlock from ldm and Attention from diffusers
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if disable:
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logging.info(f"Attention for {layer.__class__.__qualname__} not splitted")
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yield
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return
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def self_attn_forward(params: HypertileParams, scale_depth=True) -> Callable:
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latent_tile_size = max(128, tile_size) // 8
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@wraps(params.forward)
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def wrapper(*args, **kwargs):
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if not params.enabled:
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return params.forward(*args, **kwargs)
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def self_attn_forward(forward: Callable, depth: int, layer_name: str, module: nn.Module) -> Callable:
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@wraps(forward)
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def wrapper(*args, **kwargs):
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x = args[0]
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latent_tile_size = max(128, params.tile_size) // 8
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x = args[0]
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# VAE
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if x.ndim == 4:
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b, c, h, w = x.shape
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# VAE
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if x.ndim == 4:
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b, c, h, w = x.shape
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nh = random_divisor(h, latent_tile_size, swap_size)
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nw = random_divisor(w, latent_tile_size, swap_size)
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nh = random_divisor(h, latent_tile_size, params.swap_size)
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nw = random_divisor(w, latent_tile_size, params.swap_size)
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if nh * nw > 1:
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x = rearrange(x, "b c (nh h) (nw w) -> (b nh nw) c h w", nh=nh, nw=nw) # split into nh * nw tiles
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if nh * nw > 1:
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x = rearrange(x, "b c (nh h) (nw w) -> (b nh nw) c h w", nh=nh, nw=nw) # split into nh * nw tiles
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out = forward(x, *args[1:], **kwargs)
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out = params.forward(x, *args[1:], **kwargs)
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if nh * nw > 1:
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out = rearrange(out, "(b nh nw) c h w -> b c (nh h) (nw w)", nh=nh, nw=nw)
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if nh * nw > 1:
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out = rearrange(out, "(b nh nw) c h w -> b c (nh h) (nw w)", nh=nh, nw=nw)
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# U-Net
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else:
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hw: int = x.size(1)
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h, w = find_hw_candidates(hw, aspect_ratio)
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assert h * w == hw, f"Invalid aspect ratio {aspect_ratio} for input of shape {x.shape}, hw={hw}, h={h}, w={w}"
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factor = 2**depth if scale_depth else 1
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nh = random_divisor(h, latent_tile_size * factor, swap_size)
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nw = random_divisor(w, latent_tile_size * factor, swap_size)
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module._split_sizes_hypertile.append((nh, nw)) # type: ignore
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if nh * nw > 1:
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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)
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out = forward(x, *args[1:], **kwargs)
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if nh * nw > 1:
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out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw)
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out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw)
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return out
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return wrapper
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# Handle hijacking the forward method and recovering afterwards
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try:
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if is_sdxl:
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layers = DEPTH_LAYERS_XL
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# U-Net
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else:
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layers = DEPTH_LAYERS
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for depth in range(max_depth + 1):
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for layer_name, module in layer.named_modules():
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hw: int = x.size(1)
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h, w = find_hw_candidates(hw, params.aspect_ratio)
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assert h * w == hw, f"Invalid aspect ratio {params.aspect_ratio} for input of shape {x.shape}, hw={hw}, h={h}, w={w}"
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factor = 2 ** params.depth if scale_depth else 1
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nh = random_divisor(h, latent_tile_size * factor, params.swap_size)
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nw = random_divisor(w, latent_tile_size * factor, params.swap_size)
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if nh * nw > 1:
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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)
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out = params.forward(x, *args[1:], **kwargs)
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if nh * nw > 1:
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out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw)
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out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw)
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return out
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return wrapper
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def hypertile_hook_model(model: nn.Module, width, height, *, enable=False, tile_size_max=128, swap_size=1, max_depth=3, is_sdxl=False):
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hypertile_layers = getattr(model, "__webui_hypertile_layers", None)
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if hypertile_layers is None:
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if not enable:
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return
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hypertile_layers = {}
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layers = DEPTH_LAYERS_XL if is_sdxl else DEPTH_LAYERS
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for depth in range(4):
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for layer_name, module in model.named_modules():
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if any(layer_name.endswith(try_name) for try_name in layers[depth]):
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# print input shape for debugging
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logging.debug(f"HyperTile hijacking attention layer at depth {depth}: {layer_name}")
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# hijack
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module._original_forward_hypertile = module.forward
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module.forward = self_attn_forward(module.forward, depth, layer_name, module)
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module._split_sizes_hypertile = []
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yield
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finally:
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for layer_name, module in layer.named_modules():
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# remove hijack
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if hasattr(module, "_original_forward_hypertile"):
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if module._split_sizes_hypertile:
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logging.debug(f"layer {layer_name} splitted with ({module._split_sizes_hypertile})")
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# recover
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module.forward = module._original_forward_hypertile
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del module._original_forward_hypertile
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del module._split_sizes_hypertile
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params = HypertileParams()
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module.__webui_hypertile_params = params
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params.forward = module.forward
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params.depth = depth
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params.layer_name = layer_name
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module.forward = self_attn_forward(params)
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def hypertile_context_vae(model:nn.Module, aspect_ratio:float, tile_size:int, opts):
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"""
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Returns context manager for VAE
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"""
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enabled = opts.hypertile_split_vae_attn
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swap_size = opts.hypertile_swap_size_vae
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max_depth = opts.hypertile_max_depth_vae
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tile_size_max = opts.hypertile_max_tile_vae
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return split_attention(
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model,
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aspect_ratio=aspect_ratio,
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tile_size=min(tile_size, tile_size_max),
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swap_size=swap_size,
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disable=not enabled,
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max_depth=max_depth,
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is_sdxl=False,
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)
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hypertile_layers[layer_name] = 1
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def hypertile_context_unet(model:nn.Module, aspect_ratio:float, tile_size:int, opts, is_sdxl:bool):
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"""
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Returns context manager for U-Net
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"""
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enabled = opts.hypertile_split_unet_attn
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swap_size = opts.hypertile_swap_size_unet
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max_depth = opts.hypertile_max_depth_unet
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tile_size_max = opts.hypertile_max_tile_unet
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return split_attention(
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model,
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aspect_ratio=aspect_ratio,
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tile_size=min(tile_size, tile_size_max),
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swap_size=swap_size,
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disable=not enabled,
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max_depth=max_depth,
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is_sdxl=is_sdxl,
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)
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model.__webui_hypertile_layers = hypertile_layers
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aspect_ratio = width / height
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tile_size = min(largest_tile_size_available(width, height), tile_size_max)
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for layer_name, module in model.named_modules():
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if layer_name in hypertile_layers:
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params = module.__webui_hypertile_params
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params.tile_size = tile_size
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params.swap_size = swap_size
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params.aspect_ratio = aspect_ratio
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params.enabled = enable and params.depth <= max_depth
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@ -0,0 +1,73 @@
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import hypertile
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from modules import scripts, script_callbacks, shared
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class ScriptHypertile(scripts.Script):
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name = "Hypertile"
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def title(self):
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return self.name
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def show(self, is_img2img):
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return scripts.AlwaysVisible
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def process(self, p, *args):
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hypertile.set_hypertile_seed(p.all_seeds[0])
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configure_hypertile(p.width, p.height, enable_unet=shared.opts.hypertile_enable_unet)
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def before_hr(self, p, *args):
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configure_hypertile(p.hr_upscale_to_x, p.hr_upscale_to_y, enable_unet=shared.opts.hypertile_enable_unet_secondpass or shared.opts.hypertile_enable_unet)
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def configure_hypertile(width, height, enable_unet=True):
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hypertile.hypertile_hook_model(
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shared.sd_model.first_stage_model,
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width,
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height,
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swap_size=shared.opts.hypertile_swap_size_vae,
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max_depth=shared.opts.hypertile_max_depth_vae,
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tile_size_max=shared.opts.hypertile_max_tile_vae,
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enable=shared.opts.hypertile_enable_vae,
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)
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hypertile.hypertile_hook_model(
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shared.sd_model.model,
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width,
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height,
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swap_size=shared.opts.hypertile_swap_size_unet,
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max_depth=shared.opts.hypertile_max_depth_unet,
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tile_size_max=shared.opts.hypertile_max_tile_unet,
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enable=enable_unet,
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is_sdxl=shared.sd_model.is_sdxl
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)
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def on_ui_settings():
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import gradio as gr
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options = {
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"hypertile_explanation": shared.OptionHTML("""
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<a href='https://github.com/tfernd/HyperTile'>Hypertile</a> optimizes the self-attention layer within U-Net and VAE models,
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resulting in a reduction in computation time ranging from 1 to 4 times. The larger the generated image is, the greater the
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benefit.
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"""),
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"hypertile_enable_unet": shared.OptionInfo(False, "Enable Hypertile U-Net").info("noticeable change in details of the generated picture; if enabled, overrides the setting below"),
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"hypertile_enable_unet_secondpass": shared.OptionInfo(False, "Enable Hypertile U-Net for hires fix second pass"),
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"hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}),
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"hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
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"hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-net swap size", gr.Slider, {"minimum": 0, "maximum": 6, "step": 1}),
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"hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE").info("minimal change in the generated picture"),
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"hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}),
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"hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
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"hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 6, "step": 1}),
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}
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for name, opt in options.items():
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opt.section = ('hypertile', "Hypertile")
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shared.opts.add_option(name, opt)
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script_callbacks.on_ui_settings(on_ui_settings)
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@ -24,7 +24,6 @@ from modules.shared import opts, cmd_opts, state
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import modules.shared as shared
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import modules.paths as paths
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import modules.face_restoration
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from modules.hypertile import set_hypertile_seed, largest_tile_size_available, hypertile_context_unet, hypertile_context_vae
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import modules.images as images
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import modules.styles
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import modules.sd_models as sd_models
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@ -861,8 +860,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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p.comment(comment)
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p.extra_generation_params.update(model_hijack.extra_generation_params)
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set_hypertile_seed(p.seed)
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# add batch size + hypertile status to information to reproduce the run
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if p.n_iter > 1:
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shared.state.job = f"Batch {n+1} out of {p.n_iter}"
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@ -874,8 +872,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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else:
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if opts.sd_vae_decode_method != 'Full':
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p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
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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):
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x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
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x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
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x_samples_ddim = torch.stack(x_samples_ddim).float()
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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@ -1141,25 +1138,23 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
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self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
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aspect_ratio = self.width / self.height
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x = self.rng.next()
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tile_size = largest_tile_size_available(self.width, self.height)
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with hypertile_context_vae(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, opts=shared.opts):
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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):
|
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samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
|
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samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
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del x
|
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|
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if not self.enable_hr:
|
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return samples
|
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devices.torch_gc()
|
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|
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if self.latent_scale_mode is None:
|
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with hypertile_context_vae(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, opts=shared.opts):
|
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decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
|
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decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
|
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else:
|
||||
decoded_samples = None
|
||||
|
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with sd_models.SkipWritingToConfig():
|
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sd_models.reload_model_weights(info=self.hr_checkpoint_info)
|
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|
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return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
|
||||
|
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def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
|
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|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -200,14 +200,6 @@ 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"), {
|
||||
|
|
|
|||
Loading…
Reference in New Issue