Allow different merge ratios to be used for each pass. Make toggle cmd flag work again. Remove ratio flag. Remove warning about controlnet being incompatible

modules/cmd_args.py

@@ -103,5 +103,4 @@ parser.add_argument("--no-hashing", action='store_true', help="disable sha256 ha
 parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
 
 # token merging / tomesd
-parser.add_argument("--token-merging", action='store_true', help="Provides generation speedup by merging redundant tokens. (compatible with --xformers)", default=False)
-parser.add_argument("--token-merging-ratio", type=float, help="Adjusts ratio of merged to untouched tokens. Range: (0.0-1.0], Defaults to 0.5", default=0.5)
+parser.add_argument("--token-merging", action='store_true', help="Provides speed and memory improvements by merging redundant tokens. This has a more pronounced effect on higher resolutions.", default=False)

modules/processing.py

@@ -501,26 +501,16 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if k == 'sd_vae':
                 sd_vae.reload_vae_weights()
 
-        if opts.token_merging and not opts.token_merging_hr_only:
-            print("applying token merging to all passes")
-            tomesd.apply_patch(
-                p.sd_model,
-                ratio=opts.token_merging_ratio,
-                max_downsample=opts.token_merging_maximum_down_sampling,
-                sx=opts.token_merging_stride_x,
-                sy=opts.token_merging_stride_y,
-                use_rand=opts.token_merging_random,
-                merge_attn=opts.token_merging_merge_attention,
-                merge_crossattn=opts.token_merging_merge_cross_attention,
-                merge_mlp=opts.token_merging_merge_mlp
-            )
+        if (opts.token_merging or cmd_opts.token_merging) and not opts.token_merging_hr_only:
+            print("\nApplying token merging\n")
+            sd_models.apply_token_merging(sd_model=p.sd_model, hr=False)
 
         res = process_images_inner(p)
 
     finally:
         # undo model optimizations made by tomesd
-        if opts.token_merging:
-            print('removing token merging model optimizations')
+        if opts.token_merging or cmd_opts.token_merging:
+            print('\nRemoving token merging model optimizations\n')
             tomesd.remove_patch(p.sd_model)
 
         # restore opts to original state
@@ -959,20 +949,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         devices.torch_gc()
 
         # apply token merging optimizations from tomesd for high-res pass
-        # check if hr_only so we don't redundantly apply patch
-        if opts.token_merging and opts.token_merging_hr_only:
-            print("applying token merging for high-res pass")
-            tomesd.apply_patch(
-                self.sd_model,
-                ratio=opts.token_merging_ratio,
-                max_downsample=opts.token_merging_maximum_down_sampling,
-                sx=opts.token_merging_stride_x,
-                sy=opts.token_merging_stride_y,
-                use_rand=opts.token_merging_random,
-                merge_attn=opts.token_merging_merge_attention,
-                merge_crossattn=opts.token_merging_merge_cross_attention,
-                merge_mlp=opts.token_merging_merge_mlp
-            )
+        # check if hr_only so we are not redundantly patching
+        if (cmd_opts.token_merging or opts.token_merging) and (opts.token_merging_hr_only or opts.token_merging_ratio_hr != opts.token_merging_ratio):
+            # case where user wants to use separate merge ratios
+            if not opts.token_merging_hr_only:
+                # clean patch done by first pass. (clobbering the first patch might be fine? this might be excessive)
+                print('Temporarily reverting token merging optimizations in preparation for next pass')
+                tomesd.remove_patch(self.sd_model)
+
+            print("\nApplying token merging for high-res pass\n")
+            sd_models.apply_token_merging(sd_model=self.sd_model, hr=True)
 
         samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
 

modules/sd_models.py

@@ -16,6 +16,7 @@ from modules import paths, shared, modelloader, devices, script_callbacks, sd_va
 from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack
 from modules.timer import Timer
+import tomesd
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
@@ -546,3 +547,29 @@ def unload_model_weights(sd_model=None, info=None):
     print(f"Unloaded weights {timer.summary()}.")
 
     return sd_model
+
+
+def apply_token_merging(sd_model, hr: bool):
+    """
+    Applies speed and memory optimizations from tomesd.
+
+    Args:
+        hr (bool): True if called in the context of a high-res pass
+    """
+
+    ratio = shared.opts.token_merging_ratio
+    if hr:
+        ratio = shared.opts.token_merging_ratio_hr
+        print("effective hr pass merge ratio is "+str(ratio))
+
+    tomesd.apply_patch(
+        sd_model,
+        ratio=ratio,
+        max_downsample=shared.opts.token_merging_maximum_down_sampling,
+        sx=shared.opts.token_merging_stride_x,
+        sy=shared.opts.token_merging_stride_y,
+        use_rand=shared.opts.token_merging_random,
+        merge_attn=shared.opts.token_merging_merge_attention,
+        merge_crossattn=shared.opts.token_merging_merge_cross_attention,
+        merge_mlp=shared.opts.token_merging_merge_mlp
+    )

modules/shared.py

@@ -429,7 +429,7 @@ options_templates.update(options_section((None, "Hidden options"), {
 
 options_templates.update(options_section(('token_merging', 'Token Merging'), {
     "token_merging": OptionInfo(
-        False, "Enable redundant token merging via tomesd. (currently incompatible with controlnet extension)",
+        0.5, "Enable redundant token merging via tomesd. This can provide significant speed and memory improvements.",
         gr.Checkbox
     ),
     "token_merging_ratio": OptionInfo(
@@ -440,6 +440,10 @@ options_templates.update(options_section(('token_merging', 'Token Merging'), {
         True, "Apply only to high-res fix pass. Disabling can yield a ~20-35% speedup on contemporary resolutions.",
         gr.Checkbox
     ),
+    "token_merging_ratio_hr": OptionInfo(
+        0.5, "Merging Ratio (high-res pass) - If 'Apply only to high-res' is enabled, this will always be the ratio used.",
+        gr.Slider, {"minimum": 0, "maximum": 0.9, "step": 0.1}
+    ),
     # More advanced/niche settings:
     "token_merging_random": OptionInfo(
         True, "Use random perturbations - Disabling might help with certain samplers",