add hyperTile

https://github.com/tfernd/HyperTile

modules/processing.py

@@ -799,6 +799,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     infotexts = []
     output_images = []
+    unet_object = p.sd_model.model
+    vae_model = p.sd_model.first_stage_model
+    try:
+        from hyper_tile import split_attention, flush
+    except (ImportError, ModuleNotFoundError): # pip install git+https://github.com/tfernd/HyperTile@2ef64b2800d007d305755c33550537410310d7df 
+        split_attention = lambda *args, **kwargs: lambda x: x # return a no-op context manager
+        flush = lambda: None
+    import random
+    saved_rng_state = random.getstate()
+    random.seed(p.seed) # hyper_tile uses random, so we need to seed it
 
     with torch.no_grad(), p.sd_model.ema_scope():
         with devices.autocast():
@@ -866,15 +876,25 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
             with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
-                samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
+                # get largest tile size available, which is 2^x which is factor of gcd of p.width and p.height
+                gcd = math.gcd(p.width, p.height)
+                largest_tile_size_available = 1
+                while gcd % (largest_tile_size_available * 2) == 0:
+                    largest_tile_size_available *= 2
+                aspect_ratio = p.width / p.height
+                with split_attention(vae_model, aspect_ratio=aspect_ratio, tile_size=min(largest_tile_size_available, 128), disable=not shared.opts.hypertile_split_vae_attn):
+                    with split_attention(unet_object, aspect_ratio=aspect_ratio, tile_size=min(largest_tile_size_available, 256), swap_size=2, disable=not shared.opts.hypertile_split_unet_attn):
+                        flush()
+                        samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
 
             if getattr(samples_ddim, 'already_decoded', False):
                 x_samples_ddim = samples_ddim
             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 split_attention(vae_model, aspect_ratio=aspect_ratio, tile_size=min(largest_tile_size_available, 128), disable=not shared.opts.hypertile_split_vae_attn):
+                    flush()
+                    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)
@@ -980,6 +1000,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if opts.grid_save:
                 images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(use_main_prompt=True), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
+    random.setstate(saved_rng_state)
     if not p.disable_extra_networks and p.extra_network_data:
         extra_networks.deactivate(p, p.extra_network_data)
 

modules/shared_options.py

@@ -200,6 +200,8 @@ 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"),
 }))
 
 options_templates.update(options_section(('compatibility', "Compatibility"), {