Merge branch 'hypertile-in-sample' into dev

README.md

@@ -174,5 +174,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
 - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
 - LyCORIS - KohakuBlueleaf
 - Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
+- Hypertile - tfernd - https://github.com/tfernd/HyperTile
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)

extensions-builtin/hypertile/hypertile.py

@@ -1,10 +1,13 @@
 """
 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
+Warn: The patch works well only if the input image has a width and height that are multiples of 128
+Original author: @tfernd Github: https://github.com/tfernd/HyperTile
 """
 
 from __future__ import annotations
+
+import functools
+from dataclasses import dataclass
 from typing import Callable
 from typing_extensions import Literal
 
@@ -18,6 +21,19 @@ import random
 
 from einops import rearrange
 
+
+@dataclass
+class HypertileParams:
+    depth = 0
+    layer_name = ""
+    tile_size: int = 0
+    swap_size: int = 0
+    aspect_ratio: float = 1.0
+    forward = None
+    enabled = False
+
+
+
 # TODO add SD-XL layers
 DEPTH_LAYERS = {
     0: [
@@ -176,6 +192,7 @@ DEPTH_LAYERS_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
@@ -193,10 +210,13 @@ def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
 
     return ns[idx]
 
+
 def set_hypertile_seed(seed: int) -> None:
     RNG_INSTANCE.seed(seed)
 
-def largest_tile_size_available(width:int, height:int) -> int:
+
+@functools.cache
+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
@@ -207,6 +227,7 @@ def largest_tile_size_available(width:int, height:int) -> int:
         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
@@ -219,6 +240,7 @@ def iterative_closest_divisors(hw:int, aspect_ratio:float) -> tuple[int, int]:
     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]:
     """
@@ -240,132 +262,87 @@ def find_hw_candidates(hw:int, aspect_ratio:float) -> tuple[int, int]:
             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
+def self_attn_forward(params: HypertileParams, scale_depth=True) -> Callable:
 
-    latent_tile_size = max(128, tile_size) // 8
+    @wraps(params.forward)
+    def wrapper(*args, **kwargs):
+        if not params.enabled:
+            return params.forward(*args, **kwargs)
 
-    def self_attn_forward(forward: Callable, depth: int, layer_name: str, module: nn.Module) -> Callable:
-        @wraps(forward)
-        def wrapper(*args, **kwargs):
-            x = args[0]
+        latent_tile_size = max(128, params.tile_size) // 8
+        x = args[0]
 
-            # VAE
-            if x.ndim == 4:
-                b, c, h, w = x.shape
+        # 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)
+            nh = random_divisor(h, latent_tile_size, params.swap_size)
+            nw = random_divisor(w, latent_tile_size, params.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
+            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)
+            out = params.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)
+            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
+        # U-Net
         else:
-            layers = DEPTH_LAYERS
-        for depth in range(max_depth + 1):
-            for layer_name, module in layer.named_modules():
+            hw: int = x.size(1)
+            h, w = find_hw_candidates(hw, params.aspect_ratio)
+            assert h * w == hw, f"Invalid aspect ratio {params.aspect_ratio} for input of shape {x.shape}, hw={hw}, h={h}, w={w}"
+
+            factor = 2 ** params.depth if scale_depth else 1
+            nh = random_divisor(h, latent_tile_size * factor, params.swap_size)
+            nw = random_divisor(w, latent_tile_size * factor, params.swap_size)
+
+            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 = params.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
+
+
+def hypertile_hook_model(model: nn.Module, width, height, *, enable=False, tile_size_max=128, swap_size=1, max_depth=3, is_sdxl=False):
+    hypertile_layers = getattr(model, "__webui_hypertile_layers", None)
+    if hypertile_layers is None:
+        if not enable:
+            return
+
+        hypertile_layers = {}
+        layers = DEPTH_LAYERS_XL if is_sdxl else DEPTH_LAYERS
+
+        for depth in range(4):
+            for layer_name, module in model.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
+                    params = HypertileParams()
+                    module.__webui_hypertile_params = params
+                    params.forward = module.forward
+                    params.depth = depth
+                    params.layer_name = layer_name
+                    module.forward = self_attn_forward(params)
 
-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,
-    )
+                    hypertile_layers[layer_name] = 1
 
-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,
-    )
+        model.__webui_hypertile_layers = hypertile_layers
+
+    aspect_ratio = width / height
+    tile_size = min(largest_tile_size_available(width, height), tile_size_max)
+
+    for layer_name, module in model.named_modules():
+        if layer_name in hypertile_layers:
+            params = module.__webui_hypertile_params
+
+            params.tile_size = tile_size
+            params.swap_size = swap_size
+            params.aspect_ratio = aspect_ratio
+            params.enabled = enable and params.depth <= max_depth

extensions-builtin/hypertile/scripts/hypertile_script.py

@@ -0,0 +1,73 @@
+import hypertile
+from modules import scripts, script_callbacks, shared
+
+
+class ScriptHypertile(scripts.Script):
+    name = "Hypertile"
+
+    def title(self):
+        return self.name
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def process(self, p, *args):
+        hypertile.set_hypertile_seed(p.all_seeds[0])
+
+        configure_hypertile(p.width, p.height, enable_unet=shared.opts.hypertile_enable_unet)
+
+    def before_hr(self, p, *args):
+        configure_hypertile(p.hr_upscale_to_x, p.hr_upscale_to_y, enable_unet=shared.opts.hypertile_enable_unet_secondpass or shared.opts.hypertile_enable_unet)
+
+
+def configure_hypertile(width, height, enable_unet=True):
+    hypertile.hypertile_hook_model(
+        shared.sd_model.first_stage_model,
+        width,
+        height,
+        swap_size=shared.opts.hypertile_swap_size_vae,
+        max_depth=shared.opts.hypertile_max_depth_vae,
+        tile_size_max=shared.opts.hypertile_max_tile_vae,
+        enable=shared.opts.hypertile_enable_vae,
+    )
+
+    hypertile.hypertile_hook_model(
+        shared.sd_model.model,
+        width,
+        height,
+        swap_size=shared.opts.hypertile_swap_size_unet,
+        max_depth=shared.opts.hypertile_max_depth_unet,
+        tile_size_max=shared.opts.hypertile_max_tile_unet,
+        enable=enable_unet,
+        is_sdxl=shared.sd_model.is_sdxl
+    )
+
+
+def on_ui_settings():
+    import gradio as gr
+
+    options = {
+        "hypertile_explanation": shared.OptionHTML("""
+    <a href='https://github.com/tfernd/HyperTile'>Hypertile</a> optimizes the self-attention layer within U-Net and VAE models,
+    resulting in a reduction in computation time ranging from 1 to 4 times. The larger the generated image is, the greater the
+    benefit.
+    """),
+
+        "hypertile_enable_unet": shared.OptionInfo(False, "Enable Hypertile U-Net").info("noticeable change in details of the generated picture; if enabled, overrides the setting below"),
+        "hypertile_enable_unet_secondpass": shared.OptionInfo(False, "Enable Hypertile U-Net for hires fix second pass"),
+        "hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}),
+        "hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+        "hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-net swap size", gr.Slider, {"minimum": 0, "maximum": 6, "step": 1}),
+
+        "hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE").info("minimal change in the generated picture"),
+        "hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}),
+        "hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+        "hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 6, "step": 1}),
+    }
+
+    for name, opt in options.items():
+        opt.section = ('hypertile', "Hypertile")
+        shared.opts.add_option(name, opt)
+
+
+script_callbacks.on_ui_settings(on_ui_settings)

modules/processing.py

@@ -24,7 +24,6 @@ from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 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
 import modules.images as images
 import modules.styles
 import modules.sd_models as sd_models
@@ -861,8 +860,7 @@ 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}"
 
@@ -874,8 +872,7 @@ 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
-                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.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
@@ -1141,25 +1138,23 @@ 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()
-        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
+
         if not self.enable_hr:
             return samples
         devices.torch_gc()
 
         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:
             decoded_samples = None
 
         with sd_models.SkipWritingToConfig():
             sd_models.reload_model_weights(info=self.hr_checkpoint_info)
+
         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):
@@ -1244,18 +1239,15 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         if self.scripts is not None:
             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())
 
         self.sampler = None
         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
         return decoded_samples
@@ -1532,11 +1524,8 @@ 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
-        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:
             samples = samples * self.nmask + self.init_latent * self.mask

modules/shared_options.py

@@ -201,14 +201,6 @@ 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"), {