Commit d1ba46b6 authored by Robert Barron's avatar Robert Barron
Browse files

allow first pass and hires pass to use a single prompt to do different prompt... 

allow first pass and hires pass to use a single prompt to do different prompt editing, hires is 1.0..2.0:
  relative time range is [1..2]
  absolute time range is [steps+1..steps+hire_steps], e.g. with 30 steps and 20 hires steps, '20' is 2/3rds through first pass, and 40 is halfway through hires pass
parent 5a38a9c0

modules/processing.py

+12 −8
Original line number Diff line number Diff line
@@ -319,12 +319,14 @@ class StableDiffusionProcessing: 
        self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]

        self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]



    def cached_params(self, required_prompts, steps, extra_network_data):

    def cached_params(self, required_prompts, steps, hires_steps, extra_network_data, use_old_scheduling):

        """Returns parameters that invalidate the cond cache if changed"""



        return (

            required_prompts,

            steps,

            hires_steps,

            use_old_scheduling,

            opts.CLIP_stop_at_last_layers,

            shared.sd_model.sd_checkpoint_info,

            extra_network_data,
@@ -334,7 +336,7 @@ class StableDiffusionProcessing: 
            self.height,

        )



    def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data):

    def get_conds_with_caching(self, function, required_prompts, steps, hires_steps, caches, extra_network_data):

        """

        Returns the result of calling function(shared.sd_model, required_prompts, steps)

        using a cache to store the result if the same arguments have been used before.
@@ -347,7 +349,7 @@ class StableDiffusionProcessing: 
        caches is a list with items described above.

        """



        cached_params = self.cached_params(required_prompts, steps, extra_network_data)

        cached_params = self.cached_params(required_prompts, steps, hires_steps, extra_network_data, shared.opts.use_old_scheduling)



        for cache in caches:

            if cache[0] is not None and cached_params == cache[0]:
@@ -356,7 +358,7 @@ class StableDiffusionProcessing: 
        cache = caches[0]



        with devices.autocast():

            cache[1] = function(shared.sd_model, required_prompts, steps)

            cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling)



        cache[0] = cached_params

        return cache[1]
@@ -367,8 +369,9 @@ class StableDiffusionProcessing: 


        sampler_config = sd_samplers.find_sampler_config(self.sampler_name)

        self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1

        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)

        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)

        self.firstpass_steps = self.steps * self.step_multiplier

        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.firstpass_steps, None, [self.cached_uc], self.extra_network_data)

        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.firstpass_steps, None, [self.cached_c], self.extra_network_data)



    def parse_extra_network_prompts(self):

        self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)
@@ -1225,8 +1228,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): 
        hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y)

        hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True)



        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)

        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)

        hires_steps = (self.hr_second_pass_steps or self.steps) * self.step_multiplier

        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.firstpass_steps, hires_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)

        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.firstpass_steps, hires_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)



    def setup_conds(self):

        super().setup_conds()

modules/prompt_parser.py

+31 −10
Original line number Diff line number Diff line
@@ -26,7 +26,7 @@ plain: /([^\\\[\]():|]|\\.)+/ 
%import common.SIGNED_NUMBER -> NUMBER

""")



def get_learned_conditioning_prompt_schedules(prompts, steps):

def get_learned_conditioning_prompt_schedules(prompts, base_steps, hires_steps=None, use_old_scheduling=False):

    """

    >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]

    >>> g("test")
@@ -57,17 +57,38 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): 
    [[1, 'female'], [2, 'male'], [3, 'female'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'female'], [8, 'male'], [9, 'female'], [10, 'male']]

    >>> g("[fe|||]male")

    [[1, 'female'], [2, 'male'], [3, 'male'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'male'], [8, 'male'], [9, 'female'], [10, 'male']]

    >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10, 10)[0]

    >>> g("a [b:.5] c")

    [[10, 'a b c']]

    >>> g("a [b:1.5] c")

    [[5, 'a  c'], [10, 'a b c']]

    """



    if hires_steps is None or use_old_scheduling:

        int_offset = 0

        flt_offset = 0

        steps = base_steps

    else:

        int_offset = base_steps

        flt_offset = 1.0

        steps = hires_steps



    def collect_steps(steps, tree):

        res = [steps]



        class CollectSteps(lark.Visitor):

            def scheduled(self, tree):

                tree.children[-2] = float(tree.children[-2])

                if tree.children[-2] < 1:

                    tree.children[-2] *= steps

                tree.children[-2] = min(steps, int(tree.children[-2]))

                s = tree.children[-2]

                v = float(s)

                if use_old_scheduling:

                    v = v*steps if v<1 else v

                else:

                    if "." in s:

                        v = (v - flt_offset) * steps

                    else:

                        v = (v - int_offset)

                tree.children[-2] = min(steps, int(v))

                if tree.children[-2] >= 1:

                    res.append(tree.children[-2])



            def alternate(self, tree):
@@ -134,7 +155,7 @@ class SdConditioning(list): 






def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps):

def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps, hires_steps=None, use_old_scheduling=False):

    """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),

    and the sampling step at which this condition is to be replaced by the next one.
@@ -154,7 +175,7 @@ def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps): 
    """

    res = []



    prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)

    prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps, hires_steps, use_old_scheduling)

    cache = {}



    for prompt, prompt_schedule in zip(prompts, prompt_schedules):
@@ -229,7 +250,7 @@ class MulticondLearnedConditioning: 
        self.batch: List[List[ComposableScheduledPromptConditioning]] = batch





def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:

def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None, use_old_scheduling=False) -> MulticondLearnedConditioning:

    """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.

    For each prompt, the list is obtained by splitting the prompt using the AND separator.
@@ -238,7 +259,7 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne 


    res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)



    learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)

    learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps, hires_steps, use_old_scheduling)



    res = []

    for indexes in res_indexes:

modules/shared.py

+1 −0
Original line number Diff line number Diff line
@@ -516,6 +516,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), { 
    "use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),

    "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),

    "hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),

    "use_old_scheduling": OptionInfo(False, "Use old prompt where first pass and hires both used the same timeline, and < 1 meant relative and >= 1 meant absolute"),

}))



options_templates.update(options_section(('interrogate', "Interrogate"), {

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