heavily simplify

modules/generation_parameters_copypaste.py

@@ -282,33 +282,6 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         res["Hires resize-1"] = 0
         res["Hires resize-2"] = 0
 
-    # Infer additional override settings for token merging
-    token_merging_ratio = res.get("Token merging ratio", None)
-    token_merging_ratio_hr = res.get("Token merging ratio hr", None)
-
-    if token_merging_ratio is not None or token_merging_ratio_hr is not None:
-        res["Token merging"] = 'True'
-
-        if token_merging_ratio is None:
-            res["Token merging hr only"] = 'True'
-        else:
-            res["Token merging hr only"] = 'False'
-
-        if res.get("Token merging random", None) is None:
-            res["Token merging random"] = 'False'
-        if res.get("Token merging merge attention", None) is None:
-            res["Token merging merge attention"] = 'True'
-        if res.get("Token merging merge cross attention", None) is None:
-            res["Token merging merge cross attention"] = 'False'
-        if res.get("Token merging merge mlp", None) is None:
-            res["Token merging merge mlp"] = 'False'
-        if res.get("Token merging stride x", None) is None:
-            res["Token merging stride x"] = '2'
-        if res.get("Token merging stride y", None) is None:
-            res["Token merging stride y"] = '2'
-        if res.get("Token merging maximum down sampling", None) is None:
-            res["Token merging maximum down sampling"] = '1'
-
     restore_old_hires_fix_params(res)
 
     # Missing RNG means the default was set, which is GPU RNG
@@ -335,17 +308,8 @@ infotext_to_setting_name_mapping = [
     ('UniPC skip type', 'uni_pc_skip_type'),
     ('UniPC order', 'uni_pc_order'),
     ('UniPC lower order final', 'uni_pc_lower_order_final'),
-    ('Token merging', 'token_merging'),
     ('Token merging ratio', 'token_merging_ratio'),
-    ('Token merging hr only', 'token_merging_hr_only'),
     ('Token merging ratio hr', 'token_merging_ratio_hr'),
-    ('Token merging random', 'token_merging_random'),
-    ('Token merging merge attention', 'token_merging_merge_attention'),
-    ('Token merging merge cross attention', 'token_merging_merge_cross_attention'),
-    ('Token merging merge mlp', 'token_merging_merge_mlp'),
-    ('Token merging maximum down sampling', 'token_merging_maximum_down_sampling'),
-    ('Token merging stride x', 'token_merging_stride_x'),
-    ('Token merging stride y', 'token_merging_stride_y'),
     ('RNG', 'randn_source'),
     ('NGMS', 's_min_uncond')
 ]

modules/processing.py

@@ -34,7 +34,7 @@ import tomesd
 # add a logger for the processing module
 logger = logging.getLogger(__name__)
 # manually set output level here since there is no option to do so yet through launch options
-# logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)s %(name)s %(message)s')
+logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)s %(name)s %(message)s')
 
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
@@ -496,15 +496,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
         "Clip skip": None if clip_skip <= 1 else clip_skip,
         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
-        "Token merging ratio": None if not opts.token_merging or opts.token_merging_hr_only else opts.token_merging_ratio,
-        "Token merging ratio hr": None if not opts.token_merging else opts.token_merging_ratio_hr,
-        "Token merging random": None if opts.token_merging_random is False else opts.token_merging_random,
-        "Token merging merge attention": None if opts.token_merging_merge_attention is True else opts.token_merging_merge_attention,
-        "Token merging merge cross attention": None if opts.token_merging_merge_cross_attention is False else opts.token_merging_merge_cross_attention,
-        "Token merging merge mlp": None if opts.token_merging_merge_mlp is False else opts.token_merging_merge_mlp,
-        "Token merging stride x": None if opts.token_merging_stride_x == 2 else opts.token_merging_stride_x,
-        "Token merging stride y": None if opts.token_merging_stride_y == 2 else opts.token_merging_stride_y,
-        "Token merging maximum down sampling": None if opts.token_merging_maximum_down_sampling == 1 else opts.token_merging_maximum_down_sampling,
+        "Token merging ratio": None if opts.token_merging_ratio == 0 else opts.token_merging_ratio,
+        "Token merging ratio hr": None if not p.enable_hr or opts.token_merging_ratio_hr == 0 else opts.token_merging_ratio_hr,
         "Init image hash": getattr(p, 'init_img_hash', None),
         "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
         "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
@@ -538,15 +531,15 @@ 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:
+        if opts.token_merging_ratio > 0:
             sd_models.apply_token_merging(sd_model=p.sd_model, hr=False)
-            logger.debug('Token merging applied')
+            logger.debug(f"Token merging applied to first pass. Ratio: '{opts.token_merging_ratio}'")
 
         res = process_images_inner(p)
 
     finally:
         # undo model optimizations made by tomesd
-        if opts.token_merging:
+        if opts.token_merging_ratio > 0:
             tomesd.remove_patch(p.sd_model)
             logger.debug('Token merging model optimizations removed')
 
@@ -1003,19 +996,21 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         devices.torch_gc()
 
         # apply token merging optimizations from tomesd for high-res pass
-        # check if hr_only so we are not redundantly patching
-        if 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)
+        if opts.token_merging_ratio_hr > 0:
+            # in case the user has used separate merge ratios
+            if opts.token_merging_ratio > 0:
                 tomesd.remove_patch(self.sd_model)
-                logger.debug('Temporarily removed token merging optimizations in preparation for next pass')
+                logger.debug('Adjusting token merging ratio for high-res pass')
 
             sd_models.apply_token_merging(sd_model=self.sd_model, hr=True)
-            logger.debug('Applied token merging for high-res pass')
+            logger.debug(f"Applied token merging for high-res pass. Ratio: '{opts.token_merging_ratio_hr}'")
 
         samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
 
+        if opts.token_merging_ratio_hr > 0 or opts.token_merging_ratio > 0:
+            tomesd.remove_patch(self.sd_model)
+            logger.debug('Removed token merging optimizations from model')
+
         self.is_hr_pass = False
 
         return samples

modules/sd_models.py

@@ -596,11 +596,8 @@ def apply_token_merging(sd_model, hr: bool):
     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
+        use_rand=False,  # can cause issues with some samplers
+        merge_attn=True,
+        merge_crossattn=False,
+        merge_mlp=False
     )

modules/shared.py

@@ -459,47 +459,13 @@ 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. This can provide significant speed and memory improvements.",
-        gr.Checkbox
-    ),
-    "token_merging_ratio": OptionInfo(
-        0.5, "Merging Ratio",
-        gr.Slider, {"minimum": 0, "maximum": 0.9, "step": 0.1}
-    ),
-    "token_merging_hr_only": OptionInfo(
-        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.",
+        0, "Merging Ratio (high-res pass)",
         gr.Slider, {"minimum": 0, "maximum": 0.9, "step": 0.1}
     ),
-    # More advanced/niche settings:
-    "token_merging_random": OptionInfo(
-        False, "Use random perturbations - Can improve outputs for certain samplers. For others, it may cause visual artifacting.",
-        gr.Checkbox
-    ),
-    "token_merging_merge_attention": OptionInfo(
-        True, "Merge attention",
-        gr.Checkbox
-    ),
-     "token_merging_merge_cross_attention": OptionInfo(
-        False, "Merge cross attention",
-        gr.Checkbox
-    ),
-    "token_merging_merge_mlp": OptionInfo(
-        False, "Merge mlp",
-        gr.Checkbox
-    ),
-    "token_merging_maximum_down_sampling": OptionInfo(1, "Maximum down sampling", gr.Radio, lambda: {"choices": [1, 2, 4, 8]}),
-    "token_merging_stride_x": OptionInfo(
-        2, "Stride - X",
-        gr.Slider, {"minimum": 2, "maximum": 8, "step": 2}
-    ),
-    "token_merging_stride_y": OptionInfo(
-        2, "Stride - Y",
-        gr.Slider, {"minimum": 2, "maximum": 8, "step": 2}
+    "token_merging_ratio": OptionInfo(
+        0, "Merging Ratio",
+        gr.Slider, {"minimum": 0, "maximum": 0.9, "step": 0.1}
     )
 }))