mirror of
https://github.com/lllyasviel/stable-diffusion-webui-forge.git
synced 2026-04-27 17:51:22 +00:00
Merge branch 'dev' into test-fp8
This commit is contained in:
Kohaku-Blueleaf
345
extensions-builtin/hypertile/hypertile.py
Normal file
345
extensions-builtin/hypertile/hypertile.py
Normal file
@@ -0,0 +1,345 @@
|
||||
"""
|
||||
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
|
||||
Original author: @tfernd Github: https://github.com/tfernd/HyperTile
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import functools
|
||||
from dataclasses import dataclass
|
||||
from typing import Callable
|
||||
|
||||
from functools import wraps, cache
|
||||
|
||||
import math
|
||||
import torch.nn as nn
|
||||
import random
|
||||
|
||||
from einops import rearrange
|
||||
|
||||
|
||||
@dataclass
|
||||
class HypertileParams:
|
||||
depth = 0
|
||||
layer_name = ""
|
||||
tile_size: int = 0
|
||||
swap_size: int = 0
|
||||
aspect_ratio: float = 1.0
|
||||
forward = None
|
||||
enabled = False
|
||||
|
||||
|
||||
|
||||
# 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)
|
||||
|
||||
|
||||
@functools.cache
|
||||
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
|
||||
|
||||
|
||||
def self_attn_forward(params: HypertileParams, scale_depth=True) -> Callable:
|
||||
|
||||
@wraps(params.forward)
|
||||
def wrapper(*args, **kwargs):
|
||||
if not params.enabled:
|
||||
return params.forward(*args, **kwargs)
|
||||
|
||||
latent_tile_size = max(128, params.tile_size) // 8
|
||||
x = args[0]
|
||||
|
||||
# VAE
|
||||
if x.ndim == 4:
|
||||
b, c, h, w = x.shape
|
||||
|
||||
nh = random_divisor(h, latent_tile_size, params.swap_size)
|
||||
nw = random_divisor(w, latent_tile_size, params.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 = params.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, params.aspect_ratio)
|
||||
assert h * w == hw, f"Invalid aspect ratio {params.aspect_ratio} for input of shape {x.shape}, hw={hw}, h={h}, w={w}"
|
||||
|
||||
factor = 2 ** params.depth if scale_depth else 1
|
||||
nh = random_divisor(h, latent_tile_size * factor, params.swap_size)
|
||||
nw = random_divisor(w, latent_tile_size * factor, params.swap_size)
|
||||
|
||||
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 = params.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
|
||||
|
||||
|
||||
def hypertile_hook_model(model: nn.Module, width, height, *, enable=False, tile_size_max=128, swap_size=1, max_depth=3, is_sdxl=False):
|
||||
hypertile_layers = getattr(model, "__webui_hypertile_layers", None)
|
||||
if hypertile_layers is None:
|
||||
if not enable:
|
||||
return
|
||||
|
||||
hypertile_layers = {}
|
||||
layers = DEPTH_LAYERS_XL if is_sdxl else DEPTH_LAYERS
|
||||
|
||||
for depth in range(4):
|
||||
for layer_name, module in model.named_modules():
|
||||
if any(layer_name.endswith(try_name) for try_name in layers[depth]):
|
||||
params = HypertileParams()
|
||||
module.__webui_hypertile_params = params
|
||||
params.forward = module.forward
|
||||
params.depth = depth
|
||||
params.layer_name = layer_name
|
||||
module.forward = self_attn_forward(params)
|
||||
|
||||
hypertile_layers[layer_name] = 1
|
||||
|
||||
model.__webui_hypertile_layers = hypertile_layers
|
||||
|
||||
aspect_ratio = width / height
|
||||
tile_size = min(largest_tile_size_available(width, height), tile_size_max)
|
||||
|
||||
for layer_name, module in model.named_modules():
|
||||
if layer_name in hypertile_layers:
|
||||
params = module.__webui_hypertile_params
|
||||
|
||||
params.tile_size = tile_size
|
||||
params.swap_size = swap_size
|
||||
params.aspect_ratio = aspect_ratio
|
||||
params.enabled = enable and params.depth <= max_depth
|
||||
73
extensions-builtin/hypertile/scripts/hypertile_script.py
Normal file
73
extensions-builtin/hypertile/scripts/hypertile_script.py
Normal file
@@ -0,0 +1,73 @@
|
||||
import hypertile
|
||||
from modules import scripts, script_callbacks, shared
|
||||
|
||||
|
||||
class ScriptHypertile(scripts.Script):
|
||||
name = "Hypertile"
|
||||
|
||||
def title(self):
|
||||
return self.name
|
||||
|
||||
def show(self, is_img2img):
|
||||
return scripts.AlwaysVisible
|
||||
|
||||
def process(self, p, *args):
|
||||
hypertile.set_hypertile_seed(p.all_seeds[0])
|
||||
|
||||
configure_hypertile(p.width, p.height, enable_unet=shared.opts.hypertile_enable_unet)
|
||||
|
||||
def before_hr(self, p, *args):
|
||||
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)
|
||||
|
||||
|
||||
def configure_hypertile(width, height, enable_unet=True):
|
||||
hypertile.hypertile_hook_model(
|
||||
shared.sd_model.first_stage_model,
|
||||
width,
|
||||
height,
|
||||
swap_size=shared.opts.hypertile_swap_size_vae,
|
||||
max_depth=shared.opts.hypertile_max_depth_vae,
|
||||
tile_size_max=shared.opts.hypertile_max_tile_vae,
|
||||
enable=shared.opts.hypertile_enable_vae,
|
||||
)
|
||||
|
||||
hypertile.hypertile_hook_model(
|
||||
shared.sd_model.model,
|
||||
width,
|
||||
height,
|
||||
swap_size=shared.opts.hypertile_swap_size_unet,
|
||||
max_depth=shared.opts.hypertile_max_depth_unet,
|
||||
tile_size_max=shared.opts.hypertile_max_tile_unet,
|
||||
enable=enable_unet,
|
||||
is_sdxl=shared.sd_model.is_sdxl
|
||||
)
|
||||
|
||||
|
||||
def on_ui_settings():
|
||||
import gradio as gr
|
||||
|
||||
options = {
|
||||
"hypertile_explanation": shared.OptionHTML("""
|
||||
<a href='https://github.com/tfernd/HyperTile'>Hypertile</a> optimizes the self-attention layer within U-Net and VAE models,
|
||||
resulting in a reduction in computation time ranging from 1 to 4 times. The larger the generated image is, the greater the
|
||||
benefit.
|
||||
"""),
|
||||
|
||||
"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"),
|
||||
"hypertile_enable_unet_secondpass": shared.OptionInfo(False, "Enable Hypertile U-Net for hires fix second pass"),
|
||||
"hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}),
|
||||
"hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
|
||||
"hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-net swap size", gr.Slider, {"minimum": 0, "maximum": 6, "step": 1}),
|
||||
|
||||
"hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE").info("minimal change in the generated picture"),
|
||||
"hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}),
|
||||
"hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
|
||||
"hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 6, "step": 1}),
|
||||
}
|
||||
|
||||
for name, opt in options.items():
|
||||
opt.section = ('hypertile', "Hypertile")
|
||||
shared.opts.add_option(name, opt)
|
||||
|
||||
|
||||
script_callbacks.on_ui_settings(on_ui_settings)
|
||||
Reference in New Issue
Block a user