convert/add hypertile options

modules/hypertile.py

@@ -332,3 +332,39 @@ def split_attention(
                 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 = not 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 = not 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,
+    )
\ No newline at end of file

modules/processing.py

@@ -24,7 +24,7 @@ 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 split_attention, set_hypertile_seed, largest_tile_size_available
+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
@@ -874,7 +874,7 @@ 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 split_attention(p.sd_model.first_stage_model, aspect_ratio = p.width / p.height, tile_size=min(largest_tile_size_available(p.width, p.height), 128), disable=not shared.opts.hypertile_split_vae_attn, is_sdxl=shared.sd_model.is_sdxl):
+                with hypertile_context_unet(p.sd_model.first_stage_model, aspect_ratio=p.width / p.height, tile_size=largest_tile_size_available(p.width, p.height), is_sdxl=shared.sd_model.is_sdxl, 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()
@@ -1144,8 +1144,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         aspect_ratio = self.width / self.height
         x = self.rng.next()
         tile_size = largest_tile_size_available(self.width, self.height)
-        with split_attention(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=min(tile_size, 128), swap_size=1, disable=not shared.opts.hypertile_split_vae_attn, is_sdxl=shared.sd_model.is_sdxl):
-            with split_attention(self.sd_model.model, aspect_ratio=aspect_ratio, tile_size=min(tile_size, 256), swap_size=2, disable=not shared.opts.hypertile_split_unet_attn, is_sdxl=shared.sd_model.is_sdxl):
+        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.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, is_sdxl=shared.sd_model.is_sdxl, opts=shared.opts):
                 devices.torch_gc()
                 samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
         del x
@@ -1153,7 +1153,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             return samples
 
         if self.latent_scale_mode is None:
-            with split_attention(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=min(tile_size, 256), swap_size=1, disable=not shared.opts.hypertile_split_vae_attn, is_sdxl=shared.sd_model.is_sdxl):
+            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
@@ -1245,15 +1245,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         if self.scripts is not None:
             self.scripts.before_hr(self)
         tile_size = largest_tile_size_available(target_width, target_height)
-        with split_attention(self.sd_model.first_stage_model, aspect_ratio=target_width / target_height, tile_size=min(tile_size, 256), swap_size=1, disable=not opts.hypertile_split_vae_attn, is_sdxl=shared.sd_model.is_sdxl):
-            with split_attention(self.sd_model.model, aspect_ratio=target_width / target_height, tile_size=min(tile_size, 256), swap_size=3, max_depth=1,scale_depth=True, disable=not opts.hypertile_split_unet_attn, is_sdxl=shared.sd_model.is_sdxl):
+        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.first_stage_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()
-        with split_attention(self.sd_model.first_stage_model, aspect_ratio=target_width / target_height, tile_size=min(tile_size, 256), swap_size=1, disable=not opts.hypertile_split_vae_attn, is_sdxl=shared.sd_model.is_sdxl):
+        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
@@ -1533,8 +1534,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             x *= self.initial_noise_multiplier
         aspect_ratio = self.width / self.height
         tile_size = largest_tile_size_available(self.width, self.height)
-        with split_attention(self.sd_model.first_stage_model, aspect_ratio=aspect_ratio, tile_size=min(tile_size, 128), swap_size=1, disable=not shared.opts.hypertile_split_vae_attn, is_sdxl=shared.sd_model.is_sdxl):
-            with split_attention(self.sd_model.model, aspect_ratio=aspect_ratio, tile_size=min(tile_size, 256), swap_size=2, disable=not shared.opts.hypertile_split_unet_attn, is_sdxl=shared.sd_model.is_sdxl):
+        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.first_stage_model, aspect_ratio=aspect_ratio, tile_size=tile_size, is_sdxl=shared.sd_model.is_sdxl, opts=shared.opts):
                 devices.torch_gc()
                 samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
 

modules/shared_options.py

@@ -202,6 +202,12 @@ options_templates.update(options_section(('optimizations', "Optimizations"), {
     "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"), {