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Merge pull request #13948 from aria1th/hypertile-in-sample
support HyperTile optimization
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AUTOMATIC1111
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371
modules/hypertile.py
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371
modules/hypertile.py
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@ -0,0 +1,371 @@
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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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"""
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from __future__ import annotations
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from typing import Callable
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from typing_extensions import Literal
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import logging
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from functools import wraps, cache
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from contextlib import contextmanager
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import math
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import torch.nn as nn
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import random
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from einops import rearrange
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# TODO add SD-XL layers
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DEPTH_LAYERS = {
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0: [
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# SD 1.5 U-Net (diffusers)
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"down_blocks.0.attentions.0.transformer_blocks.0.attn1",
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"down_blocks.0.attentions.1.transformer_blocks.0.attn1",
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"up_blocks.3.attentions.0.transformer_blocks.0.attn1",
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"up_blocks.3.attentions.1.transformer_blocks.0.attn1",
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"up_blocks.3.attentions.2.transformer_blocks.0.attn1",
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# SD 1.5 U-Net (ldm)
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"input_blocks.1.1.transformer_blocks.0.attn1",
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"input_blocks.2.1.transformer_blocks.0.attn1",
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"output_blocks.9.1.transformer_blocks.0.attn1",
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"output_blocks.10.1.transformer_blocks.0.attn1",
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"output_blocks.11.1.transformer_blocks.0.attn1",
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# SD 1.5 VAE
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"decoder.mid_block.attentions.0",
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"decoder.mid.attn_1",
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],
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1: [
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# SD 1.5 U-Net (diffusers)
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"down_blocks.1.attentions.0.transformer_blocks.0.attn1",
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"down_blocks.1.attentions.1.transformer_blocks.0.attn1",
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"up_blocks.2.attentions.0.transformer_blocks.0.attn1",
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"up_blocks.2.attentions.1.transformer_blocks.0.attn1",
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"up_blocks.2.attentions.2.transformer_blocks.0.attn1",
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# SD 1.5 U-Net (ldm)
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"input_blocks.4.1.transformer_blocks.0.attn1",
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"input_blocks.5.1.transformer_blocks.0.attn1",
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"output_blocks.6.1.transformer_blocks.0.attn1",
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"output_blocks.7.1.transformer_blocks.0.attn1",
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"output_blocks.8.1.transformer_blocks.0.attn1",
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],
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2: [
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# SD 1.5 U-Net (diffusers)
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"down_blocks.2.attentions.0.transformer_blocks.0.attn1",
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"down_blocks.2.attentions.1.transformer_blocks.0.attn1",
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"up_blocks.1.attentions.0.transformer_blocks.0.attn1",
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"up_blocks.1.attentions.1.transformer_blocks.0.attn1",
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"up_blocks.1.attentions.2.transformer_blocks.0.attn1",
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# SD 1.5 U-Net (ldm)
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"input_blocks.7.1.transformer_blocks.0.attn1",
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"input_blocks.8.1.transformer_blocks.0.attn1",
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"output_blocks.3.1.transformer_blocks.0.attn1",
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"output_blocks.4.1.transformer_blocks.0.attn1",
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"output_blocks.5.1.transformer_blocks.0.attn1",
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],
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3: [
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# SD 1.5 U-Net (diffusers)
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"mid_block.attentions.0.transformer_blocks.0.attn1",
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# SD 1.5 U-Net (ldm)
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"middle_block.1.transformer_blocks.0.attn1",
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],
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}
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# XL layers, thanks for GitHub@gel-crabs for the help
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DEPTH_LAYERS_XL = {
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0: [
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# SD 1.5 U-Net (diffusers)
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"down_blocks.0.attentions.0.transformer_blocks.0.attn1",
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"down_blocks.0.attentions.1.transformer_blocks.0.attn1",
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"up_blocks.3.attentions.0.transformer_blocks.0.attn1",
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"up_blocks.3.attentions.1.transformer_blocks.0.attn1",
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"up_blocks.3.attentions.2.transformer_blocks.0.attn1",
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# SD 1.5 U-Net (ldm)
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"input_blocks.4.1.transformer_blocks.0.attn1",
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"input_blocks.5.1.transformer_blocks.0.attn1",
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"output_blocks.3.1.transformer_blocks.0.attn1",
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"output_blocks.4.1.transformer_blocks.0.attn1",
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"output_blocks.5.1.transformer_blocks.0.attn1",
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# SD 1.5 VAE
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"decoder.mid_block.attentions.0",
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"decoder.mid.attn_1",
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],
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1: [
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# SD 1.5 U-Net (diffusers)
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#"down_blocks.1.attentions.0.transformer_blocks.0.attn1",
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#"down_blocks.1.attentions.1.transformer_blocks.0.attn1",
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#"up_blocks.2.attentions.0.transformer_blocks.0.attn1",
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#"up_blocks.2.attentions.1.transformer_blocks.0.attn1",
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#"up_blocks.2.attentions.2.transformer_blocks.0.attn1",
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# SD 1.5 U-Net (ldm)
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"input_blocks.4.1.transformer_blocks.1.attn1",
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"input_blocks.5.1.transformer_blocks.1.attn1",
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"output_blocks.3.1.transformer_blocks.1.attn1",
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"output_blocks.4.1.transformer_blocks.1.attn1",
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"output_blocks.5.1.transformer_blocks.1.attn1",
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"input_blocks.7.1.transformer_blocks.0.attn1",
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"input_blocks.8.1.transformer_blocks.0.attn1",
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"output_blocks.0.1.transformer_blocks.0.attn1",
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"output_blocks.1.1.transformer_blocks.0.attn1",
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"output_blocks.2.1.transformer_blocks.0.attn1",
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"input_blocks.7.1.transformer_blocks.1.attn1",
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"input_blocks.8.1.transformer_blocks.1.attn1",
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"output_blocks.0.1.transformer_blocks.1.attn1",
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"output_blocks.1.1.transformer_blocks.1.attn1",
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"output_blocks.2.1.transformer_blocks.1.attn1",
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"input_blocks.7.1.transformer_blocks.2.attn1",
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"input_blocks.8.1.transformer_blocks.2.attn1",
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"output_blocks.0.1.transformer_blocks.2.attn1",
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"output_blocks.1.1.transformer_blocks.2.attn1",
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"output_blocks.2.1.transformer_blocks.2.attn1",
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"input_blocks.7.1.transformer_blocks.3.attn1",
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"input_blocks.8.1.transformer_blocks.3.attn1",
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"output_blocks.0.1.transformer_blocks.3.attn1",
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"output_blocks.1.1.transformer_blocks.3.attn1",
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"output_blocks.2.1.transformer_blocks.3.attn1",
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"input_blocks.7.1.transformer_blocks.4.attn1",
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"input_blocks.8.1.transformer_blocks.4.attn1",
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"output_blocks.0.1.transformer_blocks.4.attn1",
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"output_blocks.1.1.transformer_blocks.4.attn1",
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"output_blocks.2.1.transformer_blocks.4.attn1",
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"input_blocks.7.1.transformer_blocks.5.attn1",
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"input_blocks.8.1.transformer_blocks.5.attn1",
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"output_blocks.0.1.transformer_blocks.5.attn1",
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"output_blocks.1.1.transformer_blocks.5.attn1",
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"output_blocks.2.1.transformer_blocks.5.attn1",
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"input_blocks.7.1.transformer_blocks.6.attn1",
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"input_blocks.8.1.transformer_blocks.6.attn1",
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"output_blocks.0.1.transformer_blocks.6.attn1",
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"output_blocks.1.1.transformer_blocks.6.attn1",
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"output_blocks.2.1.transformer_blocks.6.attn1",
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"input_blocks.7.1.transformer_blocks.7.attn1",
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"input_blocks.8.1.transformer_blocks.7.attn1",
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"output_blocks.0.1.transformer_blocks.7.attn1",
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"output_blocks.1.1.transformer_blocks.7.attn1",
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"output_blocks.2.1.transformer_blocks.7.attn1",
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"input_blocks.7.1.transformer_blocks.8.attn1",
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"input_blocks.8.1.transformer_blocks.8.attn1",
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"output_blocks.0.1.transformer_blocks.8.attn1",
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"output_blocks.1.1.transformer_blocks.8.attn1",
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"output_blocks.2.1.transformer_blocks.8.attn1",
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"input_blocks.7.1.transformer_blocks.9.attn1",
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"input_blocks.8.1.transformer_blocks.9.attn1",
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"output_blocks.0.1.transformer_blocks.9.attn1",
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"output_blocks.1.1.transformer_blocks.9.attn1",
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"output_blocks.2.1.transformer_blocks.9.attn1",
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],
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2: [
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# SD 1.5 U-Net (diffusers)
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"mid_block.attentions.0.transformer_blocks.0.attn1",
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# SD 1.5 U-Net (ldm)
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"middle_block.1.transformer_blocks.0.attn1",
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"middle_block.1.transformer_blocks.1.attn1",
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"middle_block.1.transformer_blocks.2.attn1",
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"middle_block.1.transformer_blocks.3.attn1",
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"middle_block.1.transformer_blocks.4.attn1",
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"middle_block.1.transformer_blocks.5.attn1",
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"middle_block.1.transformer_blocks.6.attn1",
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"middle_block.1.transformer_blocks.7.attn1",
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"middle_block.1.transformer_blocks.8.attn1",
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"middle_block.1.transformer_blocks.9.attn1",
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],
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3 : [] # TODO - separate layers for SD-XL
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}
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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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x * min_value <= value
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if max_options is 1, the behavior is deterministic
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"""
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min_value = min(min_value, value)
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# All big divisors of value (inclusive)
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divisors = [i for i in range(min_value, value + 1) if value % i == 0] # divisors in small -> big order
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ns = [value // i for i in divisors[:max_options]] # has at least 1 element # big -> small order
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idx = RNG_INSTANCE.randint(0, len(ns) - 1)
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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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"""
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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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"""
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gcd = math.gcd(width, height)
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largest_tile_size_available = 1
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while gcd % (largest_tile_size_available * 2) == 0:
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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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We check all possible divisors of hw and return the closest to the aspect ratio
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"""
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divisors = [i for i in range(2, hw + 1) if hw % i == 0] # all divisors of hw
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pairs = [(i, hw // i) for i in divisors] # all pairs of divisors of hw
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ratios = [w/h for h, w in pairs] # all ratios of pairs of divisors of hw
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closest_ratio = min(ratios, key=lambda x: abs(x - aspect_ratio)) # closest ratio to aspect_ratio
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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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Finds h and w such that h*w = hw and h/w = aspect_ratio
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"""
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h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
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# find h and w such that h*w = hw and h/w = aspect_ratio
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if h * w != hw:
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w_candidate = hw / h
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# check if w is an integer
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if not w_candidate.is_integer():
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h_candidate = hw / w
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# check if h is an integer
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if not h_candidate.is_integer():
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return iterative_closest_divisors(hw, aspect_ratio)
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else:
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h = int(h_candidate)
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else:
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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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latent_tile_size = max(128, tile_size) // 8
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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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# 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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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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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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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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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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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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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,
|
||||
disable=not enabled,
|
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max_depth=max_depth,
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is_sdxl=is_sdxl,
|
||||
)
|
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@ -24,6 +24,7 @@ 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
|
||||
import modules.face_restoration
|
||||
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
|
||||
import modules.styles
|
||||
import modules.sd_models as sd_models
|
||||
@ -799,7 +800,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
|
||||
infotexts = []
|
||||
output_images = []
|
||||
|
||||
with torch.no_grad(), p.sd_model.ema_scope():
|
||||
with devices.autocast():
|
||||
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
|
||||
@ -861,7 +861,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
p.comment(comment)
|
||||
|
||||
p.extra_generation_params.update(model_hijack.extra_generation_params)
|
||||
|
||||
set_hypertile_seed(p.seed)
|
||||
# add batch size + hypertile status to information to reproduce the run
|
||||
if p.n_iter > 1:
|
||||
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
|
||||
|
||||
@ -873,8 +874,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
else:
|
||||
if opts.sd_vae_decode_method != 'Full':
|
||||
p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
|
||||
|
||||
x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
|
||||
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):
|
||||
x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
|
||||
|
||||
x_samples_ddim = torch.stack(x_samples_ddim).float()
|
||||
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
@ -1140,24 +1141,25 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
|
||||
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
|
||||
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
|
||||
|
||||
aspect_ratio = self.width / self.height
|
||||
x = self.rng.next()
|
||||
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
|
||||
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(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
|
||||
del x
|
||||
|
||||
if not self.enable_hr:
|
||||
return samples
|
||||
devices.torch_gc()
|
||||
|
||||
if self.latent_scale_mode is None:
|
||||
decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
|
||||
with hypertile_context_vae(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, opts=shared.opts):
|
||||
decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
|
||||
else:
|
||||
decoded_samples = None
|
||||
|
||||
with sd_models.SkipWritingToConfig():
|
||||
sd_models.reload_model_weights(info=self.hr_checkpoint_info)
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
|
||||
|
||||
def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
|
||||
@ -1165,7 +1167,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
return samples
|
||||
|
||||
self.is_hr_pass = True
|
||||
|
||||
target_width = self.hr_upscale_to_x
|
||||
target_height = self.hr_upscale_to_y
|
||||
|
||||
@ -1243,18 +1244,20 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
|
||||
if self.scripts is not None:
|
||||
self.scripts.before_hr(self)
|
||||
|
||||
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)
|
||||
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)
|
||||
|
||||
sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
|
||||
|
||||
self.sampler = None
|
||||
devices.torch_gc()
|
||||
|
||||
decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
|
||||
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)
|
||||
|
||||
self.is_hr_pass = False
|
||||
|
||||
return decoded_samples
|
||||
|
||||
def close(self):
|
||||
@ -1529,8 +1532,11 @@ 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
|
||||
|
||||
samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
|
||||
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)
|
||||
|
||||
if self.mask is not None:
|
||||
samples = samples * self.nmask + self.init_latent * self.mask
|
||||
|
||||
@ -201,6 +201,14 @@ 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
Block a user