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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

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papuSpartan
2023-04-04 02:26:44 -05:00
parent c707b7df95
commit 5c8e53d5e9
4 changed files with 49 additions and 33 deletions
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44
modules/processing.py
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@@ -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)