|
|
|
|
|
import math
|
|
from einops import rearrange
|
|
|
|
from torch import randint
|
|
|
|
def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
|
|
min_value = min(min_value, value)
|
|
|
|
|
|
divisors = [i for i in range(min_value, value + 1) if value % i == 0]
|
|
|
|
ns = [value // i for i in divisors[:max_options]]
|
|
|
|
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,
|
|
}
|
|
|
