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| # https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py | |
| #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: | |
| 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" | |
| 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, | |
| } | |