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#Taken from: https://github.com/tfernd/HyperTile/
import math
from einops import rearrange
# Use torch rng for consistency across generations
from torch import randint
def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
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]
ns = [value // i for i in divisors[:max_options]] # has at least 1 element
if len(ns) - 1 > 0:
idx = randint(low=0, high=len(ns) - 1, size=(1,)).item()
else:
idx = 0
return ns[idx]
class HyperTile:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}),
"swap_size": ("INT", {"default": 2, "min": 1, "max": 128}),
"max_depth": ("INT", {"default": 0, "min": 0, "max": 10}),
"scale_depth": ("BOOLEAN", {"default": False}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "model_patches/unet"
def patch(self, model, tile_size, swap_size, max_depth, scale_depth):
model_channels = model.model.model_config.unet_config["model_channels"]
latent_tile_size = max(32, tile_size) // 8
self.temp = None
def hypertile_in(q, k, v, extra_options):
model_chans = q.shape[-2]
orig_shape = extra_options['original_shape']
apply_to = []
for i in range(max_depth + 1):
apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i)))
if model_chans in apply_to:
shape = extra_options["original_shape"]
aspect_ratio = shape[-1] / shape[-2]
hw = q.size(1)
h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
factor = (2 ** apply_to.index(model_chans)) 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)
if nh * nw > 1:
q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)
self.temp = (nh, nw, h, w)
return q, k, v
return q, k, v
def hypertile_out(out, extra_options):
if self.temp is not None:
nh, nw, h, w = self.temp
self.temp = None
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
m = model.clone()
m.set_model_attn1_patch(hypertile_in)
m.set_model_attn1_output_patch(hypertile_out)
return (m, )
NODE_CLASS_MAPPINGS = {
"HyperTile": HyperTile,
}
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