From 9fd6c1e3430f5947add23e2e94ac816c2546481c Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 17 May 2023 20:22:38 +0300 Subject: [PATCH] move some settings to the new Optimization page add slider for token merging for img2img rework StableDiffusionProcessing to have the token_merging_ratio field fix a bug with applying png optimizations for live previews when they shouldn't be applied --- modules/processing.py | 52 +++++++++++++++++++++---------------------- modules/progress.py | 6 ++++- modules/sd_models.py | 34 +++++++++++++++------------- modules/shared.py | 8 +++++-- 4 files changed, 55 insertions(+), 45 deletions(-) diff --git a/modules/processing.py b/modules/processing.py index cd63b9a6..2b8dd361 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -29,12 +29,6 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion from einops import repeat, rearrange from blendmodes.blend import blendLayers, BlendType -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') # some of those options should not be changed at all because they would break the model, so I removed them from options. @@ -156,6 +150,8 @@ class StableDiffusionProcessing: self.override_settings_restore_afterwards = override_settings_restore_afterwards self.is_using_inpainting_conditioning = False self.disable_extra_networks = False + self.token_merging_ratio = 0 + self.token_merging_ratio_hr = 0 if not seed_enable_extras: self.subseed = -1 @@ -171,6 +167,7 @@ class StableDiffusionProcessing: self.all_subseeds = None self.iteration = 0 self.is_hr_pass = False + self.sampler = None @property @@ -280,6 +277,12 @@ class StableDiffusionProcessing: def close(self): self.sampler = None + def get_token_merging_ratio(self, for_hr=False): + if for_hr: + return self.token_merging_ratio_hr or opts.token_merging_ratio_hr or self.token_merging_ratio or opts.token_merging_ratio + + return self.token_merging_ratio or opts.token_merging_ratio + class Processed: def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""): @@ -309,6 +312,8 @@ class Processed: self.styles = p.styles self.job_timestamp = state.job_timestamp self.clip_skip = opts.CLIP_stop_at_last_layers + self.token_merging_ratio = p.token_merging_ratio + self.token_merging_ratio_hr = p.token_merging_ratio_hr self.eta = p.eta self.ddim_discretize = p.ddim_discretize @@ -367,6 +372,9 @@ class Processed: def infotext(self, p: StableDiffusionProcessing, index): return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size) + def get_token_merging_ratio(self, for_hr=False): + return self.token_merging_ratio_hr if for_hr else self.token_merging_ratio + # from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3 def slerp(val, low, high): @@ -480,6 +488,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers) enable_hr = getattr(p, 'enable_hr', False) + token_merging_ratio = p.get_token_merging_ratio() + token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True) uses_ensd = opts.eta_noise_seed_delta != 0 if uses_ensd: @@ -502,8 +512,8 @@ 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": opts.eta_noise_seed_delta if uses_ensd else None, - "Token merging ratio": None if opts.token_merging_ratio == 0 else opts.token_merging_ratio, - "Token merging ratio hr": None if not enable_hr or opts.token_merging_ratio_hr == 0 else opts.token_merging_ratio_hr, + "Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio, + "Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else 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, @@ -536,17 +546,12 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if k == 'sd_vae': sd_vae.reload_vae_weights() - if opts.token_merging_ratio > 0: - sd_models.apply_token_merging(sd_model=p.sd_model, hr=False) - logger.debug(f"Token merging applied to first pass. Ratio: '{opts.token_merging_ratio}'") + sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio()) res = process_images_inner(p) finally: - # undo model optimizations made by tomesd - if opts.token_merging_ratio > 0: - tomesd.remove_patch(p.sd_model) - logger.debug('Token merging model optimizations removed') + sd_models.apply_token_merging(p.sd_model, 0) # restore opts to original state if p.override_settings_restore_afterwards: @@ -996,21 +1001,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x = None devices.torch_gc() - # apply token merging optimizations from tomesd for high-res pass - 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('Adjusting token merging ratio for high-res pass') - - sd_models.apply_token_merging(sd_model=self.sd_model, hr=True) - logger.debug(f"Applied token merging for high-res pass. Ratio: '{opts.token_merging_ratio_hr}'") + sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_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) - 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') + sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio()) self.is_hr_pass = False @@ -1173,3 +1168,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): devices.torch_gc() return samples + + def get_token_merging_ratio(self, for_hr=False): + return self.token_merging_ratio or ("token_merging_ratio" in self.override_settings and opts.token_merging_ratio) or opts.token_merging_ratio_img2img or opts.token_merging_ratio diff --git a/modules/progress.py b/modules/progress.py index 269863c9..f405f07f 100644 --- a/modules/progress.py +++ b/modules/progress.py @@ -98,7 +98,11 @@ def progressapi(req: ProgressRequest): if opts.live_previews_image_format == "png": # using optimize for large images takes an enormous amount of time - save_kwargs = {"optimize": max(*image.size) > 256} + if max(*image.size) <= 256: + save_kwargs = {"optimize": True} + else: + save_kwargs = {"optimize": False, "compress_level": 1} + else: save_kwargs = {} diff --git a/modules/sd_models.py b/modules/sd_models.py index e612be10..4bd8783e 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -583,23 +583,27 @@ def unload_model_weights(sd_model=None, info=None): return sd_model -def apply_token_merging(sd_model, hr: bool): +def apply_token_merging(sd_model, token_merging_ratio): """ Applies speed and memory optimizations from tomesd. - - Args: - hr (bool): True if called in the context of a high-res pass """ - ratio = shared.opts.token_merging_ratio - if hr: - ratio = shared.opts.token_merging_ratio_hr + current_token_merging_ratio = getattr(sd_model, 'applied_token_merged_ratio', 0) - tomesd.apply_patch( - sd_model, - ratio=ratio, - use_rand=False, # can cause issues with some samplers - merge_attn=True, - merge_crossattn=False, - merge_mlp=False - ) + if current_token_merging_ratio == token_merging_ratio: + return + + if current_token_merging_ratio > 0: + tomesd.remove_patch(sd_model) + + if token_merging_ratio > 0: + tomesd.apply_patch( + sd_model, + ratio=token_merging_ratio, + use_rand=False, # can cause issues with some samplers + merge_attn=True, + merge_crossattn=False, + merge_mlp=False + ) + + sd_model.applied_token_merged_ratio = token_merging_ratio diff --git a/modules/shared.py b/modules/shared.py index 47bc6d0e..76af8b9c 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -413,8 +413,13 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP nrtwork; 1 ignores none, 2 ignores one layer"), "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"), "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different vidocard vendors"), +})) + +options_templates.update(options_section(('optimizations', "Optimizations"), { + "s_min_uncond": OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"), "token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"), - "token_merging_ratio_hr": OptionInfo(0.0, "Togen merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}), + "token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"), + "token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"), })) options_templates.update(options_section(('compatibility', "Compatibility"), { @@ -498,7 +503,6 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" "eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; higher = more unperdictable results"), "eta_ancestral": OptionInfo(1.0, "Eta for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; applies to Euler a and other samplers that have a in them"), "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}), - 's_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"), 's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),