heavily simplify

1 files changed, 15 insertions, 20 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 6828e898..32ff61e9 100644
--- a/modules/processing.py
+++ b/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