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
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AUTOMATIC
parent
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9fd6c1e343
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@ -29,12 +29,6 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
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from einops import repeat, rearrange
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from blendmodes.blend import blendLayers, BlendType
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import tomesd
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# add a logger for the processing module
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logger = logging.getLogger(__name__)
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# manually set output level here since there is no option to do so yet through launch options
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# logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)s %(name)s %(message)s')
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# some of those options should not be changed at all because they would break the model, so I removed them from options.
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@ -156,6 +150,8 @@ class StableDiffusionProcessing:
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self.override_settings_restore_afterwards = override_settings_restore_afterwards
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self.is_using_inpainting_conditioning = False
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self.disable_extra_networks = False
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self.token_merging_ratio = 0
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self.token_merging_ratio_hr = 0
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if not seed_enable_extras:
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self.subseed = -1
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@ -171,6 +167,7 @@ class StableDiffusionProcessing:
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self.all_subseeds = None
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self.iteration = 0
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self.is_hr_pass = False
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self.sampler = None
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@property
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@ -280,6 +277,12 @@ class StableDiffusionProcessing:
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def close(self):
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self.sampler = None
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def get_token_merging_ratio(self, for_hr=False):
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if for_hr:
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return self.token_merging_ratio_hr or opts.token_merging_ratio_hr or self.token_merging_ratio or opts.token_merging_ratio
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return self.token_merging_ratio or opts.token_merging_ratio
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class Processed:
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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=""):
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@ -309,6 +312,8 @@ class Processed:
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self.styles = p.styles
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self.job_timestamp = state.job_timestamp
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self.clip_skip = opts.CLIP_stop_at_last_layers
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self.token_merging_ratio = p.token_merging_ratio
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self.token_merging_ratio_hr = p.token_merging_ratio_hr
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self.eta = p.eta
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self.ddim_discretize = p.ddim_discretize
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@ -367,6 +372,9 @@ class Processed:
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def infotext(self, p: StableDiffusionProcessing, index):
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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)
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def get_token_merging_ratio(self, for_hr=False):
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return self.token_merging_ratio_hr if for_hr else self.token_merging_ratio
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# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
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def slerp(val, low, high):
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@ -480,6 +488,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
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clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
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enable_hr = getattr(p, 'enable_hr', False)
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token_merging_ratio = p.get_token_merging_ratio()
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token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True)
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uses_ensd = opts.eta_noise_seed_delta != 0
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if uses_ensd:
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@ -502,8 +512,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
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"Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
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"Clip skip": None if clip_skip <= 1 else clip_skip,
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"ENSD": opts.eta_noise_seed_delta if uses_ensd else None,
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"Token merging ratio": None if opts.token_merging_ratio == 0 else opts.token_merging_ratio,
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"Token merging ratio hr": None if not enable_hr or opts.token_merging_ratio_hr == 0 else opts.token_merging_ratio_hr,
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"Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio,
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"Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
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"Init image hash": getattr(p, 'init_img_hash', None),
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"RNG": opts.randn_source if opts.randn_source != "GPU" else None,
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"NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
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@ -536,17 +546,12 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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if k == 'sd_vae':
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sd_vae.reload_vae_weights()
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if opts.token_merging_ratio > 0:
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sd_models.apply_token_merging(sd_model=p.sd_model, hr=False)
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logger.debug(f"Token merging applied to first pass. Ratio: '{opts.token_merging_ratio}'")
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sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
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res = process_images_inner(p)
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finally:
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# undo model optimizations made by tomesd
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if opts.token_merging_ratio > 0:
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tomesd.remove_patch(p.sd_model)
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logger.debug('Token merging model optimizations removed')
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sd_models.apply_token_merging(p.sd_model, 0)
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# restore opts to original state
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if p.override_settings_restore_afterwards:
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@ -996,21 +1001,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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x = None
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devices.torch_gc()
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# apply token merging optimizations from tomesd for high-res pass
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if opts.token_merging_ratio_hr > 0:
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# in case the user has used separate merge ratios
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if opts.token_merging_ratio > 0:
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tomesd.remove_patch(self.sd_model)
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logger.debug('Adjusting token merging ratio for high-res pass')
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sd_models.apply_token_merging(sd_model=self.sd_model, hr=True)
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logger.debug(f"Applied token merging for high-res pass. Ratio: '{opts.token_merging_ratio_hr}'")
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sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
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samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
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if opts.token_merging_ratio_hr > 0 or opts.token_merging_ratio > 0:
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tomesd.remove_patch(self.sd_model)
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logger.debug('Removed token merging optimizations from model')
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sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
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self.is_hr_pass = False
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@ -1173,3 +1168,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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devices.torch_gc()
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return samples
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def get_token_merging_ratio(self, for_hr=False):
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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
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@ -98,7 +98,11 @@ def progressapi(req: ProgressRequest):
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if opts.live_previews_image_format == "png":
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# using optimize for large images takes an enormous amount of time
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save_kwargs = {"optimize": max(*image.size) > 256}
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if max(*image.size) <= 256:
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save_kwargs = {"optimize": True}
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else:
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save_kwargs = {"optimize": False, "compress_level": 1}
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else:
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save_kwargs = {}
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@ -583,23 +583,27 @@ def unload_model_weights(sd_model=None, info=None):
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return sd_model
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def apply_token_merging(sd_model, hr: bool):
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def apply_token_merging(sd_model, token_merging_ratio):
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"""
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Applies speed and memory optimizations from tomesd.
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Args:
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hr (bool): True if called in the context of a high-res pass
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"""
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ratio = shared.opts.token_merging_ratio
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if hr:
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ratio = shared.opts.token_merging_ratio_hr
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current_token_merging_ratio = getattr(sd_model, 'applied_token_merged_ratio', 0)
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tomesd.apply_patch(
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sd_model,
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ratio=ratio,
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use_rand=False, # can cause issues with some samplers
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merge_attn=True,
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merge_crossattn=False,
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merge_mlp=False
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)
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if current_token_merging_ratio == token_merging_ratio:
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return
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if current_token_merging_ratio > 0:
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tomesd.remove_patch(sd_model)
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if token_merging_ratio > 0:
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tomesd.apply_patch(
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sd_model,
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ratio=token_merging_ratio,
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use_rand=False, # can cause issues with some samplers
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merge_attn=True,
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merge_crossattn=False,
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merge_mlp=False
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)
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sd_model.applied_token_merged_ratio = token_merging_ratio
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@ -413,8 +413,13 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"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"),
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"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
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"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"),
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}))
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options_templates.update(options_section(('optimizations', "Optimizations"), {
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"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"),
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"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"),
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"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}),
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"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"),
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"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"),
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}))
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options_templates.update(options_section(('compatibility', "Compatibility"), {
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@ -498,7 +503,6 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
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"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"),
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"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"),
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"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
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'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"),
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's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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