From d1ba46b6e15d2f2917e7d392955c8c6f988527ba Mon Sep 17 00:00:00 2001 From: Robert Barron Date: Wed, 9 Aug 2023 07:46:30 -0700 Subject: 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 --- modules/prompt_parser.py | 41 +++++++++++++++++++++++++++++++---------- 1 file changed, 31 insertions(+), 10 deletions(-) (limited to 'modules/prompt_parser.py') diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index 32d214e3..e8c41f38 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -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") @@ -57,18 +57,39 @@ 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])) - res.append(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): res.extend(range(1, steps+1)) @@ -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: -- cgit v1.2.1