Commit 9fd6c1e3 authored by AUTOMATIC's avatar AUTOMATIC
Browse files

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
parent f6c06e3e

modules/processing.py

+25 −27
Original line number Diff line number Diff line
@@ -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

modules/progress.py

+5 −1
Original line number Diff line number Diff line
@@ -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 = {}

modules/sd_models.py

+20 −16
Original line number Diff line number Diff line
@@ -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)



    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=ratio,

            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

modules/shared.py

+6 −2
Original line number Diff line number Diff line
@@ -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}),

CRA Git | Maintained and supported by SUSTech CRA and CCSE