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Transition to using settings through UI instead of cmd line args. Added feature to only apply to hr-fix. Install package using requirements_versions.txt

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papuSpartan 2023-04-01 22:18:35 -05:00
parent 8c88bf4006
commit a609bd56b4
5 changed files with 80 additions and 10 deletions
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35
modules/processing.py
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@ -29,6 +29,7 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
from einops import repeat, rearrange
from blendmodes.blend import blendLayers, BlendType
import tomesd
# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
@ -500,9 +501,28 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
if k == 'sd_vae':
sd_vae.reload_vae_weights()
if opts.token_merging:
if p.hr_second_pass_steps < 1 and not opts.token_merging_hr_only:
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
)
res = process_images_inner(p)
finally:
# undo model optimizations made by tomesd
if opts.token_merging:
tomesd.remove_patch(p.sd_model)
# restore opts to original state
if p.override_settings_restore_afterwards:
for k, v in stored_opts.items():
@ -938,6 +958,21 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
x = None
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:
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
)
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
return samples