Merge pull request #13948 from aria1th/hypertile-in-sample (fd8674a4) · Commits · github_fork / Stable Diffusion Webui

modules/hypertile.py

0 → 100644

+371 −0

Original line number	Original line	Diff line number	Diff line
			"""
			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
			Author : @tfernd Github : https://github.com/tfernd/HyperTile
			"""

			from __future__ import annotations
			from typing import Callable
			from typing_extensions import Literal

			import logging
			from functools import wraps, cache
			from contextlib import contextmanager

			import math
			import torch.nn as nn
			import random

			from einops import rearrange

			# 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)

			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

			@contextmanager
			def split_attention(
			layer: nn.Module,
			/,
			aspect_ratio: float, # width/height
			tile_size: int = 128, # 128 for VAE
			swap_size: int = 1, # 1 for VAE
			*,
			disable: bool = False,
			max_depth: Literal[0, 1, 2, 3] = 0, # ! Try 0 or 1
			scale_depth: bool = True, # scale the tile-size depending on the depth
			is_sdxl: bool = False, # is the model SD-XL
			):
			# Hijacks AttnBlock from ldm and Attention from diffusers

			if disable:
			logging.info(f"Attention for {layer.__class__.__qualname__} not splitted")
			yield
			return

			latent_tile_size = max(128, tile_size) // 8

			def self_attn_forward(forward: Callable, depth: int, layer_name: str, module: nn.Module) -> Callable:
			@wraps(forward)
			def wrapper(args, *kwargs):
			x = args[0]

			# VAE
			if x.ndim == 4:
			b, c, h, w = x.shape

			nh = random_divisor(h, latent_tile_size, swap_size)
			nw = random_divisor(w, latent_tile_size, 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 = 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, aspect_ratio)
			assert h * w == hw, f"Invalid aspect ratio {aspect_ratio} for input of shape {x.shape}, hw={hw}, h={h}, w={w}"

			factor = 2**depth 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)

			module._split_sizes_hypertile.append((nh, nw)) # type: ignore

			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 = 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

			# Handle hijacking the forward method and recovering afterwards
			try:
			if is_sdxl:
			layers = DEPTH_LAYERS_XL
			else:
			layers = DEPTH_LAYERS
			for depth in range(max_depth + 1):
			for layer_name, module in layer.named_modules():
			if any(layer_name.endswith(try_name) for try_name in layers[depth]):
			# print input shape for debugging
			logging.debug(f"HyperTile hijacking attention layer at depth {depth}: {layer_name}")
			# hijack
			module._original_forward_hypertile = module.forward
			module.forward = self_attn_forward(module.forward, depth, layer_name, module)
			module._split_sizes_hypertile = []
			yield
			finally:
			for layer_name, module in layer.named_modules():
			# remove hijack
			if hasattr(module, "_original_forward_hypertile"):
			if module._split_sizes_hypertile:
			logging.debug(f"layer {layer_name} splitted with ({module._split_sizes_hypertile})")
			# recover
			module.forward = module._original_forward_hypertile
			del module._original_forward_hypertile
			del module._split_sizes_hypertile

			def hypertile_context_vae(model:nn.Module, aspect_ratio:float, tile_size:int, opts):
			"""
			Returns context manager for VAE
			"""
			enabled = opts.hypertile_split_vae_attn
			swap_size = opts.hypertile_swap_size_vae
			max_depth = opts.hypertile_max_depth_vae
			tile_size_max = opts.hypertile_max_tile_vae
			return split_attention(
			model,
			aspect_ratio=aspect_ratio,
			tile_size=min(tile_size, tile_size_max),
			swap_size=swap_size,
			disable=not enabled,
			max_depth=max_depth,
			is_sdxl=False,
			)

			def hypertile_context_unet(model:nn.Module, aspect_ratio:float, tile_size:int, opts, is_sdxl:bool):
			"""
			Returns context manager for U-Net
			"""
			enabled = opts.hypertile_split_unet_attn
			swap_size = opts.hypertile_swap_size_unet
			max_depth = opts.hypertile_max_depth_unet
			tile_size_max = opts.hypertile_max_tile_unet
			return split_attention(
			model,
			aspect_ratio=aspect_ratio,
			tile_size=min(tile_size, tile_size_max),
			swap_size=swap_size,
			disable=not enabled,
			max_depth=max_depth,
			is_sdxl=is_sdxl,
			)

modules/processing.py

+25 −19

Original line number	Original line	Diff line number	Diff line
	@@ -24,6 +24,7 @@ from modules.shared import opts, cmd_opts, state
	import modules.shared as shared		import modules.shared as shared
	import modules.paths as paths		import modules.paths as paths
	import modules.face_restoration		import modules.face_restoration
			from modules.hypertile import set_hypertile_seed, largest_tile_size_available, hypertile_context_unet, hypertile_context_vae
	import modules.images as images		import modules.images as images
	import modules.styles		import modules.styles
	import modules.sd_models as sd_models		import modules.sd_models as sd_models
	@@ -799,7 +800,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:

	infotexts = []		infotexts = []
	output_images = []		output_images = []

	with torch.no_grad(), p.sd_model.ema_scope():		with torch.no_grad(), p.sd_model.ema_scope():
	with devices.autocast():		with devices.autocast():
	p.init(p.all_prompts, p.all_seeds, p.all_subseeds)		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.comment(comment)

	p.extra_generation_params.update(model_hijack.extra_generation_params)		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:		if p.n_iter > 1:
	shared.state.job = f"Batch {n+1} out of {p.n_iter}"		shared.state.job = f"Batch {n+1} out of {p.n_iter}"

	@@ -873,7 +874,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
	else:		else:
	if opts.sd_vae_decode_method != 'Full':		if opts.sd_vae_decode_method != 'Full':
	p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method		p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
			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 = 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.stack(x_samples_ddim).float()
	@@ -1140,24 +1141,25 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

	def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):		def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
	self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)		self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
			aspect_ratio = self.width / self.height
	x = self.rng.next()		x = self.rng.next()
			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))		samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
	del x		del x

	if not self.enable_hr:		if not self.enable_hr:
	return samples		return samples
			devices.torch_gc()

	if self.latent_scale_mode is None:		if self.latent_scale_mode is None:
			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)		decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
	else:		else:
	decoded_samples = None		decoded_samples = None

	with sd_models.SkipWritingToConfig():		with sd_models.SkipWritingToConfig():
	sd_models.reload_model_weights(info=self.hr_checkpoint_info)		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)		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):		def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
	@@ -1165,7 +1167,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
	return samples		return samples

	self.is_hr_pass = True		self.is_hr_pass = True

	target_width = self.hr_upscale_to_x		target_width = self.hr_upscale_to_x
	target_height = self.hr_upscale_to_y		target_height = self.hr_upscale_to_y

	@@ -1243,18 +1244,20 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

	if self.scripts is not None:		if self.scripts is not None:
	self.scripts.before_hr(self)		self.scripts.before_hr(self)
			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)		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())		sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())

	self.sampler = None		self.sampler = None
	devices.torch_gc()		devices.torch_gc()
			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)		decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)

	self.is_hr_pass = False		self.is_hr_pass = False

	return decoded_samples		return decoded_samples

	def close(self):		def close(self):
	@@ -1529,7 +1532,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
	if self.initial_noise_multiplier != 1.0:		if self.initial_noise_multiplier != 1.0:
	self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier		self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier
	x *= self.initial_noise_multiplier		x *= self.initial_noise_multiplier
			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)		samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)

	if self.mask is not None:		if self.mask is not None:

modules/shared_options.py

+8 −0

Original line number	Original line	Diff line number	Diff line
	@@ -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"),		"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"),		"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"),		"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"), {		options_templates.update(options_section(('compatibility', "Compatibility"), {

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