StableDiffusion/WebUI
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

heavily simplify

Browse Source
This commit is contained in:
papuSpartan 2023-05-13 10:23:42 -05:00
parent 55e52c878a
commit ac83627a31
4 changed files with 23 additions and 101 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

36
modules/generation_parameters_copypaste.py
View File

@ -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-1"] = 0
res["Hires resize-2"] = 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) restore_old_hires_fix_params(res)
# Missing RNG means the default was set, which is GPU RNG # 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 skip type', 'uni_pc_skip_type'),
('UniPC order', 'uni_pc_order'), ('UniPC order', 'uni_pc_order'),
('UniPC lower order final', 'uni_pc_lower_order_final'), ('UniPC lower order final', 'uni_pc_lower_order_final'),
('Token merging', 'token_merging'),
('Token merging ratio', 'token_merging_ratio'), ('Token merging ratio', 'token_merging_ratio'),
('Token merging hr only', 'token_merging_hr_only'),
('Token merging ratio hr', 'token_merging_ratio_hr'), ('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'), ('RNG', 'randn_source'),
('NGMS', 's_min_uncond') ('NGMS', 's_min_uncond')
] ]

35
modules/processing.py
View File

@ -34,7 +34,7 @@ import tomesd
# add a logger for the processing module # add a logger for the processing module
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
# manually set output level here since there is no option to do so yet through launch options # 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. # 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, "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, "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, "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": None if opts.token_merging_ratio == 0 else opts.token_merging_ratio,
"Token merging ratio hr": None if not opts.token_merging else opts.token_merging_ratio_hr, "Token merging ratio hr": None if not p.enable_hr or opts.token_merging_ratio_hr == 0 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,
"Init image hash": getattr(p, 'init_img_hash', None), "Init image hash": getattr(p, 'init_img_hash', None),
"RNG": opts.randn_source if opts.randn_source != "GPU" else 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, "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': if k == 'sd_vae':
sd_vae.reload_vae_weights() 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) 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) res = process_images_inner(p)
finally: finally:
# undo model optimizations made by tomesd # undo model optimizations made by tomesd
if opts.token_merging: if opts.token_merging_ratio > 0:
tomesd.remove_patch(p.sd_model) tomesd.remove_patch(p.sd_model)
logger.debug('Token merging model optimizations removed') logger.debug('Token merging model optimizations removed')
@ -1003,19 +996,21 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
devices.torch_gc() devices.torch_gc()
# apply token merging optimizations from tomesd for high-res pass # apply token merging optimizations from tomesd for high-res pass
# check if hr_only so we are not redundantly patching if opts.token_merging_ratio_hr > 0:
if opts.token_merging and (opts.token_merging_hr_only or opts.token_merging_ratio_hr != opts.token_merging_ratio): # in case the user has used separate merge ratios
# case where user wants to use separate merge ratios if opts.token_merging_ratio > 0:
if not opts.token_merging_hr_only:
# clean patch done by first pass. (clobbering the first patch might be fine? this might be excessive)
tomesd.remove_patch(self.sd_model) 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) 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) 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 self.is_hr_pass = False
return samples return samples

11
modules/sd_models.py
View File

@ -596,11 +596,8 @@ def apply_token_merging(sd_model, hr: bool):
tomesd.apply_patch( tomesd.apply_patch(
sd_model, sd_model,
ratio=ratio, ratio=ratio,
max_downsample=shared.opts.token_merging_maximum_down_sampling, use_rand=False, # can cause issues with some samplers
sx=shared.opts.token_merging_stride_x, merge_attn=True,
sy=shared.opts.token_merging_stride_y, merge_crossattn=False,
use_rand=shared.opts.token_merging_random, merge_mlp=False
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
) )

42
modules/shared.py
View File

@ -459,47 +459,13 @@ options_templates.update(options_section((None, "Hidden options"), {
})) }))
options_templates.update(options_section(('token_merging', 'Token Merging'), { options_templates.update(options_section(('token_merging', 'Token Merging'), {
"token_merging": OptionInfo( "token_merging_ratio_hr": OptionInfo(
False, "Enable redundant token merging via tomesd. This can provide significant speed and memory improvements.", 0, "Merging Ratio (high-res pass)",
gr.Checkbox gr.Slider, {"minimum": 0, "maximum": 0.9, "step": 0.1}
), ),
"token_merging_ratio": OptionInfo( "token_merging_ratio": OptionInfo(
0.5, "Merging Ratio", 0, "Merging Ratio",
gr.Slider, {"minimum": 0, "maximum": 0.9, "step": 0.1} 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.",
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}
) )
})) }))