rework hires prompts/sampler code to among other things support different extra networks in first/second pass
rework quoting for infotext items that have commas in them to use json (should be backwards compatible except for cases where it didn't work previously) add some locals from processing function into the Processing class as fields
This commit is contained in:
AUTOMATIC
parent
5ec2c294ee
commit
ff0e17174f
@ -1,5 +1,6 @@
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import base64
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import io
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import json
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import os
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import re
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@ -34,13 +35,20 @@ def reset():
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def quote(text):
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if ',' not in str(text):
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if ',' not in str(text) and '\n' not in str(text):
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return text
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text = str(text)
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text = text.replace('\\', '\\\\')
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text = text.replace('"', '\\"')
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return f'"{text}"'
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return json.dumps(text, ensure_ascii=False)
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def unquote(text):
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if len(text) == 0 or text[0] != '"' or text[-1] != '"':
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return text
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try:
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return json.loads(text)
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except Exception:
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return text
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def image_from_url_text(filedata):
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@ -261,7 +269,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
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res["Negative prompt"] = negative_prompt
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for k, v in re_param.findall(lastline):
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v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
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if v[0] == '"' and v[-1] == '"':
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v = unquote(v)
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m = re_imagesize.match(v)
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if m is not None:
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res[f"{k}-1"] = m.group(1)
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@ -269,11 +279,6 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
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else:
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res[k] = v
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if k.startswith("Hires prompt"):
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res["Hires prompt"] = v[1:][:-1].replace(';', ',')
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elif k.startswith("Hires negative prompt"):
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res["Hires negative prompt"] = v[1:][:-1].replace(';', ',')
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# Missing CLIP skip means it was set to 1 (the default)
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if "Clip skip" not in res:
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res["Clip skip"] = "1"
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@ -286,6 +291,15 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
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res["Hires resize-1"] = 0
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res["Hires resize-2"] = 0
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if "Hires sampler" not in res:
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res["Hires sampler"] = "Use same sampler"
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if "Hires prompt" not in res:
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res["Hires prompt"] = ""
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if "Hires negative prompt" not in res:
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res["Hires negative prompt"] = ""
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restore_old_hires_fix_params(res)
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# Missing RNG means the default was set, which is GPU RNG
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@ -169,6 +169,16 @@ class StableDiffusionProcessing:
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self.is_hr_pass = False
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self.sampler = None
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self.prompts = None
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self.negative_prompts = None
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self.seeds = None
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self.subseeds = None
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self.step_multiplier = 1
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self.cached_uc = [None, None]
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self.cached_c = [None, None]
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self.uc = None
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self.c = None
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@property
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def sd_model(self):
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@ -271,11 +281,15 @@ class StableDiffusionProcessing:
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def init(self, all_prompts, all_seeds, all_subseeds):
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pass
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts, hr_conditioning=None, hr_unconditional_conditioning=None):
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
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raise NotImplementedError()
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def close(self):
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self.sampler = None
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self.c = None
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self.uc = None
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self.cached_c = [None, None]
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self.cached_uc = [None, 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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@ -283,6 +297,52 @@ class StableDiffusionProcessing:
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return self.token_merging_ratio or opts.token_merging_ratio
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def setup_prompts(self):
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if type(self.prompt) == list:
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self.all_prompts = self.prompt
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else:
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self.all_prompts = self.batch_size * self.n_iter * [self.prompt]
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if type(self.negative_prompt) == list:
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self.all_negative_prompts = self.negative_prompt
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else:
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self.all_negative_prompts = self.batch_size * self.n_iter * [self.negative_prompt]
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self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]
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self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]
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def get_conds_with_caching(self, function, required_prompts, steps, cache):
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"""
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Returns the result of calling function(shared.sd_model, required_prompts, steps)
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using a cache to store the result if the same arguments have been used before.
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cache is an array containing two elements. The first element is a tuple
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representing the previously used arguments, or None if no arguments
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have been used before. The second element is where the previously
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computed result is stored.
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"""
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if cache[0] is not None and (required_prompts, steps) == cache[0]:
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return cache[1]
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with devices.autocast():
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cache[1] = function(shared.sd_model, required_prompts, steps)
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cache[0] = (required_prompts, steps)
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return cache[1]
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def setup_conds(self):
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sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
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self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
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self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.negative_prompts, self.steps * self.step_multiplier, self.cached_uc)
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self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.prompts, self.steps * self.step_multiplier, self.cached_c)
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def parse_extra_network_prompts(self):
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self.prompts, extra_network_data = extra_networks.parse_prompts(self.prompts)
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return extra_network_data
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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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@ -582,29 +642,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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comments = {}
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if type(p.prompt) == list:
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p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
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else:
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p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)]
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if type(p.negative_prompt) == list:
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p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
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else:
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p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
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if type(p) == StableDiffusionProcessingTxt2Img:
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if p.enable_hr and p.hr_prompt == '':
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p.all_hr_prompts, p.all_hr_negative_prompts = p.all_prompts, p.all_negative_prompts
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elif p.enable_hr and p.hr_prompt != '':
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if type(p.prompt) == list:
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p.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.hr_prompt]
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else:
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p.all_hr_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.hr_prompt, p.styles)]
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if type(p.negative_prompt) == list:
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p.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.hr_negative_prompt]
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else:
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p.all_hr_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.hr_negative_prompt, p.styles)]
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p.setup_prompts()
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if type(seed) == list:
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p.all_seeds = seed
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@ -628,29 +666,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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infotexts = []
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output_images = []
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cached_uc = [None, None]
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cached_c = [None, None]
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def get_conds_with_caching(function, required_prompts, steps, cache):
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"""
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Returns the result of calling function(shared.sd_model, required_prompts, steps)
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using a cache to store the result if the same arguments have been used before.
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cache is an array containing two elements. The first element is a tuple
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representing the previously used arguments, or None if no arguments
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have been used before. The second element is where the previously
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computed result is stored.
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"""
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if cache[0] is not None and (required_prompts, steps) == cache[0]:
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return cache[1]
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with devices.autocast():
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cache[1] = function(shared.sd_model, required_prompts, steps)
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cache[0] = (required_prompts, steps)
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return cache[1]
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with torch.no_grad(), p.sd_model.ema_scope():
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with devices.autocast():
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p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
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@ -672,40 +687,25 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if state.interrupted:
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break
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prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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if type(p) == StableDiffusionProcessingTxt2Img:
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if p.enable_hr:
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if p.hr_prompt == '':
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hr_prompts, hr_negative_prompts = prompts, negative_prompts
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else:
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hr_prompts = p.all_hr_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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hr_negative_prompts = p.all_hr_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
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subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
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p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
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p.subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
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if p.scripts is not None:
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p.scripts.before_process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
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p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
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if len(prompts) == 0:
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if len(p.prompts) == 0:
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break
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prompts, extra_network_data = extra_networks.parse_prompts(prompts)
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if type(p) == StableDiffusionProcessingTxt2Img:
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if p.enable_hr and hr_prompts != prompts:
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_, hr_extra_network_data = extra_networks.parse_prompts(hr_prompts)
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extra_network_data.update(hr_extra_network_data)
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extra_network_data = p.parse_extra_network_prompts()
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if not p.disable_extra_networks:
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with devices.autocast():
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extra_networks.activate(p, extra_network_data)
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if p.scripts is not None:
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p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
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p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
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# params.txt should be saved after scripts.process_batch, since the
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# infotext could be modified by that callback
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@ -716,18 +716,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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processed = Processed(p, [], p.seed, "")
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file.write(processed.infotext(p, 0))
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sampler_config = sd_samplers.find_sampler_config(p.sampler_name)
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step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
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uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
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c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
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if type(p) == StableDiffusionProcessingTxt2Img:
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if p.enable_hr:
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if prompts != hr_prompts:
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hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, p.steps, cached_uc)
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hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, p.steps, cached_c)
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else:
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hr_uc, hr_c = uc, c
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p.setup_conds()
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if len(model_hijack.comments) > 0:
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for comment in model_hijack.comments:
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@ -736,15 +725,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if p.n_iter > 1:
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shared.state.job = f"Batch {n+1} out of {p.n_iter}"
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with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
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if type(p) == StableDiffusionProcessingTxt2Img:
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if p.enable_hr:
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samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=hr_c, hr_unconditional_conditioning=hr_uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
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else:
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samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
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else:
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samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
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samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
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x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
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for x in x_samples_ddim:
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@ -771,7 +753,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if p.restore_faces:
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if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
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images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
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images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
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devices.torch_gc()
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@ -788,13 +770,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if p.color_corrections is not None and i < len(p.color_corrections):
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if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
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image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
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images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
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images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
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image = apply_color_correction(p.color_corrections[i], image)
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image = apply_overlay(image, p.paste_to, i, p.overlay_images)
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if opts.samples_save and not p.do_not_save_samples:
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images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
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images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p)
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text = infotext(n, i)
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infotexts.append(text)
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@ -807,10 +789,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
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if opts.save_mask:
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images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
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images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
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if opts.save_mask_composite:
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images.save_image(image_mask_composite, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
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images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
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if opts.return_mask:
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output_images.append(image_mask)
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@ -879,7 +861,7 @@ def old_hires_fix_first_pass_dimensions(width, height):
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class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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sampler = None
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def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler: str = '---', hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
|
||||
def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.enable_hr = enable_hr
|
||||
self.denoising_strength = denoising_strength
|
||||
@ -890,9 +872,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
self.hr_resize_y = hr_resize_y
|
||||
self.hr_upscale_to_x = hr_resize_x
|
||||
self.hr_upscale_to_y = hr_resize_y
|
||||
self.hr_sampler = hr_sampler
|
||||
self.hr_prompt = hr_prompt if hr_prompt != '' else ''
|
||||
self.hr_negative_prompt = hr_negative_prompt if hr_negative_prompt != '' else ''
|
||||
self.hr_sampler_name = hr_sampler_name
|
||||
self.hr_prompt = hr_prompt
|
||||
self.hr_negative_prompt = hr_negative_prompt
|
||||
self.all_hr_prompts = None
|
||||
self.all_hr_negative_prompts = None
|
||||
|
||||
@ -906,14 +888,23 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
self.truncate_y = 0
|
||||
self.applied_old_hires_behavior_to = None
|
||||
|
||||
self.hr_prompts = None
|
||||
self.hr_negative_prompts = None
|
||||
self.hr_extra_network_data = None
|
||||
|
||||
self.hr_c = None
|
||||
self.hr_uc = None
|
||||
|
||||
def init(self, all_prompts, all_seeds, all_subseeds):
|
||||
if self.enable_hr:
|
||||
if self.hr_sampler != '---':
|
||||
self.extra_generation_params["Hires sampler"] = self.hr_sampler
|
||||
if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name:
|
||||
self.extra_generation_params["Hires sampler"] = self.hr_sampler_name
|
||||
|
||||
if self.hr_prompt != '':
|
||||
self.extra_generation_params["Hires prompt"] = f'({self.hr_prompt.replace(",", ";")})'
|
||||
self.extra_generation_params["Hires negative prompt"] = f'({self.hr_negative_prompt.replace(",", ";")})'
|
||||
if tuple(self.hr_prompt) != tuple(self.prompt):
|
||||
self.extra_generation_params["Hires prompt"] = self.hr_prompt
|
||||
|
||||
if tuple(self.hr_negative_prompt) != tuple(self.negative_prompt):
|
||||
self.extra_generation_params["Hires negative prompt"] = self.hr_negative_prompt
|
||||
|
||||
if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
|
||||
self.hr_resize_x = self.width
|
||||
@ -975,7 +966,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
if self.hr_upscaler is not None:
|
||||
self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
|
||||
|
||||
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts, hr_conditioning=None, hr_unconditional_conditioning=None):
|
||||
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
|
||||
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
|
||||
|
||||
latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
|
||||
@ -1044,16 +1035,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
|
||||
shared.state.nextjob()
|
||||
|
||||
img2img_sampler_name = self.sampler_name
|
||||
img2img_sampler_name = self.hr_sampler_name or self.sampler_name
|
||||
|
||||
if self.sampler_name in ['PLMS', 'UniPC']: # PLMS/UniPC do not support img2img so we just silently switch to DDIM
|
||||
img2img_sampler_name = 'DDIM'
|
||||
|
||||
if self.hr_sampler == '---':
|
||||
pass
|
||||
else:
|
||||
img2img_sampler_name = self.hr_sampler
|
||||
|
||||
self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
|
||||
|
||||
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
|
||||
@ -1064,9 +1050,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
x = None
|
||||
devices.torch_gc()
|
||||
|
||||
if not self.disable_extra_networks:
|
||||
with devices.autocast():
|
||||
extra_networks.activate(self, self.hr_extra_network_data)
|
||||
|
||||
sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
|
||||
|
||||
samples = self.sampler.sample_img2img(self, samples, noise, hr_conditioning, hr_unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
|
||||
samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
|
||||
|
||||
sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
|
||||
|
||||
@ -1074,6 +1064,53 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
|
||||
return samples
|
||||
|
||||
def close(self):
|
||||
self.hr_c = None
|
||||
self.hr_uc = None
|
||||
|
||||
def setup_prompts(self):
|
||||
super().setup_prompts()
|
||||
|
||||
if not self.enable_hr:
|
||||
return
|
||||
|
||||
if self.hr_prompt == '':
|
||||
self.hr_prompt = self.prompt
|
||||
|
||||
if self.hr_negative_prompt == '':
|
||||
self.hr_negative_prompt = self.negative_prompt
|
||||
|
||||
if type(self.hr_prompt) == list:
|
||||
self.all_hr_prompts = self.hr_prompt
|
||||
else:
|
||||
self.all_hr_prompts = self.batch_size * self.n_iter * [self.hr_prompt]
|
||||
|
||||
if type(self.hr_negative_prompt) == list:
|
||||
self.all_hr_negative_prompts = self.hr_negative_prompt
|
||||
else:
|
||||
self.all_hr_negative_prompts = self.batch_size * self.n_iter * [self.hr_negative_prompt]
|
||||
|
||||
self.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_hr_prompts]
|
||||
self.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_hr_negative_prompts]
|
||||
|
||||
def setup_conds(self):
|
||||
super().setup_conds()
|
||||
|
||||
if self.enable_hr:
|
||||
self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, self.cached_uc)
|
||||
self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, self.cached_c)
|
||||
|
||||
def parse_extra_network_prompts(self):
|
||||
res = super().parse_extra_network_prompts()
|
||||
|
||||
if self.enable_hr:
|
||||
self.hr_prompts = self.all_hr_prompts[self.iteration * self.batch_size:(self.iteration + 1) * self.batch_size]
|
||||
self.hr_negative_prompts = self.all_hr_negative_prompts[self.iteration * self.batch_size:(self.iteration + 1) * self.batch_size]
|
||||
|
||||
self.hr_prompts, self.hr_extra_network_data = extra_networks.parse_prompts(self.hr_prompts)
|
||||
|
||||
return res
|
||||
|
||||
|
||||
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
||||
sampler = None
|
||||
|
||||
@ -454,6 +454,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
|
||||
"extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks").info("in pixels"),
|
||||
"extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
|
||||
"extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
|
||||
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_restart(),
|
||||
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *hypernetworks]}, refresh=reload_hypernetworks),
|
||||
}))
|
||||
|
||||
@ -481,8 +482,9 @@ options_templates.update(options_section(('ui', "User interface"), {
|
||||
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_restart(),
|
||||
"ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
|
||||
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
|
||||
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
|
||||
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_restart(),
|
||||
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order").needs_restart(),
|
||||
"hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires sampler selection").needs_restart(),
|
||||
"hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_restart(),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('infotext', "Infotext"), {
|
||||
|
||||
@ -39,7 +39,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
|
||||
hr_second_pass_steps=hr_second_pass_steps,
|
||||
hr_resize_x=hr_resize_x,
|
||||
hr_resize_y=hr_resize_y,
|
||||
hr_sampler=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else '---',
|
||||
hr_sampler_name=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else None,
|
||||
hr_prompt=hr_prompt,
|
||||
hr_negative_prompt=hr_negative_prompt,
|
||||
override_settings=override_settings,
|
||||
|
||||
@ -499,16 +499,16 @@ def create_ui():
|
||||
hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
|
||||
hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
|
||||
|
||||
with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact"):
|
||||
hr_sampler_index = gr.Dropdown(label='Hires sampling method', elem_id=f"hr_sampler", choices=["---"] + [x.name for x in samplers_for_img2img], value="---", type="index")
|
||||
with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
|
||||
hr_sampler_index = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + [x.name for x in samplers_for_img2img], value="Use same sampler", type="index")
|
||||
|
||||
with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact"):
|
||||
with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
|
||||
with gr.Column(scale=80):
|
||||
with gr.Row():
|
||||
hr_prompt = gr.Textbox(label="Prompt", elem_id=f"hires_prompt", show_label=False, lines=3, placeholder="Prompt that will be used for hires fix pass (leave it blank to use the same prompt as in initial txt2img gen)")
|
||||
hr_prompt = gr.Textbox(label="Prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.")
|
||||
with gr.Column(scale=80):
|
||||
with gr.Row():
|
||||
hr_negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt that will be used for hires fix pass (leave it blank to use the same prompt as in initial txt2img gen)")
|
||||
hr_negative_prompt = gr.Textbox(label="Negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.")
|
||||
|
||||
elif category == "batch":
|
||||
if not opts.dimensions_and_batch_together:
|
||||
@ -646,9 +646,11 @@ def create_ui():
|
||||
(hr_second_pass_steps, "Hires steps"),
|
||||
(hr_resize_x, "Hires resize-1"),
|
||||
(hr_resize_y, "Hires resize-2"),
|
||||
(hr_sampler_index, "Hires sampling method"),
|
||||
(hr_sampler_index, "Hires sampler"),
|
||||
(hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" else gr.update()),
|
||||
(hr_prompt, "Hires prompt"),
|
||||
(hr_negative_prompt, "Hires negative prompt"),
|
||||
(hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
|
||||
*modules.scripts.scripts_txt2img.infotext_fields
|
||||
]
|
||||
parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user