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feat: add support for lora inline prompt references (#2323)

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* Adding support to inline prompt references

* Added unittests

* Added an initial documentation for development guidelines

* Added a negative number

* renamed parameter

* removed wrongly committed file

* Code fixes

* Fixed circular reference

* Fixed typo. Added TODO

* Fixed merge

* Code cleanup

* Added missing refernce function

* Removed function from util.py... again...

* Update modules/async_worker.py

Implemented suggested change

Co-authored-by: Manuel Schmid <9307310+mashb1t@users.noreply.github.com>

* Removed another circular reference

* Renamed module

* Addressed PR comments

* Added return type to function

* refactor: move apply_wildcards to module util

* refactor: code cleanup, unify usage of tuples in lora list

* docs: add instructions for running unittests on embedded python, code cleanup

* refactor: code cleanup, move makedirs_with_log back to util

---------

Co-authored-by: cantor-set <cantor-set@no-email.net>
Co-authored-by: Manuel Schmid <9307310+mashb1t@users.noreply.github.com>
Co-authored-by: Manuel Schmid <dev@mash1t.de>
This commit is contained in:
cantor-set
2024-05-18 17:19:46 +02:00
committed by GitHub
co-authored by Manuel Schmid cantor-set Manuel Schmid
parent 3a55e7e391
commit 3bae73e23e
9 changed files with 176 additions and 73 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/async_worker.py
+31 -15
View File
@@ -4,6 +4,7 @@ from modules.patch import PatchSettings, patch_settings, patch_all
patch_all()
class AsyncTask:
def __init__(self, args):
self.args = args
@@ -44,11 +45,12 @@ def worker():
import args_manager
from extras.censor import censor_batch, censor_single
from modules.sdxl_styles import apply_style, get_random_style, apply_wildcards, fooocus_expansion, apply_arrays, random_style_name
from modules.sdxl_styles import apply_style, get_random_style, fooocus_expansion, apply_arrays, random_style_name
from modules.private_logger import log
from extras.expansion import safe_str
from modules.util import remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil, \
get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix, get_enabled_loras
from modules.util import (remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil,
get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix, get_enabled_loras,
parse_lora_references_from_prompt, apply_wildcards)
from modules.upscaler import perform_upscale
from modules.flags import Performance
from modules.meta_parser import get_metadata_parser, MetadataScheme
@@ -69,7 +71,8 @@ def worker():
print(f'[Fooocus] {text}')
async_task.yields.append(['preview', (number, text, None)])
def yield_result(async_task, imgs, black_out_nsfw, censor=True, do_not_show_finished_images=False, progressbar_index=13):
def yield_result(async_task, imgs, black_out_nsfw, censor=True, do_not_show_finished_images=False,
progressbar_index=13):
if not isinstance(imgs, list):
imgs = [imgs]
@@ -152,7 +155,8 @@ def worker():
base_model_name = args.pop()
refiner_model_name = args.pop()
refiner_switch = args.pop()
loras = get_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop())] for _ in range(modules.config.default_max_lora_number)])
loras = get_enabled_loras([(bool(args.pop()), str(args.pop()), float(args.pop())) for _ in
range(modules.config.default_max_lora_number)])
input_image_checkbox = args.pop()
current_tab = args.pop()
uov_method = args.pop()
@@ -202,7 +206,8 @@ def worker():
inpaint_erode_or_dilate = args.pop()
save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False
metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
metadata_scheme = MetadataScheme(
args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
cn_tasks = {x: [] for x in flags.ip_list}
for _ in range(flags.controlnet_image_count):
@@ -433,13 +438,16 @@ def worker():
extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []
progressbar(async_task, 3, 'Loading models ...')
loras = parse_lora_references_from_prompt(prompt, loras, modules.config.default_max_lora_number)
pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
loras=loras, base_model_additional_loras=base_model_additional_loras,
use_synthetic_refiner=use_synthetic_refiner, vae_name=vae_name)
progressbar(async_task, 3, 'Processing prompts ...')
tasks = []
for i in range(image_number):
if disable_seed_increment:
task_seed = seed % (constants.MAX_SEED + 1)
@@ -450,8 +458,10 @@ def worker():
task_prompt = apply_wildcards(prompt, task_rng, i, read_wildcards_in_order)
task_prompt = apply_arrays(task_prompt, i)
task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, read_wildcards_in_order)
task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_positive_prompts]
task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_negative_prompts]
task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in
extra_positive_prompts]
task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in
extra_negative_prompts]
positive_basic_workloads = []
negative_basic_workloads = []
@@ -652,7 +662,8 @@ def worker():
)
if debugging_inpaint_preprocessor:
yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), black_out_nsfw, do_not_show_finished_images=True)
yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), black_out_nsfw,
do_not_show_finished_images=True)
return
progressbar(async_task, 13, 'VAE Inpaint encoding ...')
@@ -807,7 +818,8 @@ def worker():
done_steps = current_task_id * steps + step
async_task.yields.append(['preview', (
int(15.0 + 85.0 * float(done_steps) / float(all_steps)),
f'Step {step}/{total_steps} in the {current_task_id + 1}{ordinal_suffix(current_task_id + 1)} Sampling', y)])
f'Step {step}/{total_steps} in the {current_task_id + 1}{ordinal_suffix(current_task_id + 1)} Sampling',
y)])
for current_task_id, task in enumerate(tasks):
execution_start_time = time.perf_counter()
@@ -862,7 +874,8 @@ def worker():
d = [('Prompt', 'prompt', task['log_positive_prompt']),
('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
('Styles', 'styles', str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])),
('Styles', 'styles',
str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])),
('Performance', 'performance', performance_selection.value)]
if performance_selection.steps() != steps:
@@ -885,7 +898,8 @@ def worker():
if refiner_swap_method != flags.refiner_swap_method:
d.append(('Refiner Swap Method', 'refiner_swap_method', refiner_swap_method))
if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr:
d.append(('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
d.append(
('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
d.append(('Sampler', 'sampler', sampler_name))
d.append(('Scheduler', 'scheduler', scheduler_name))
@@ -905,11 +919,13 @@ def worker():
metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
task['log_negative_prompt'], task['negative'],
steps, base_model_name, refiner_model_name, loras, vae_name)
d.append(('Metadata Scheme', 'metadata_scheme', metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
d.append(('Metadata Scheme', 'metadata_scheme',
metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
img_paths.append(log(x, d, metadata_parser, output_format, task))
yield_result(async_task, img_paths, black_out_nsfw, False, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
yield_result(async_task, img_paths, black_out_nsfw, False,
do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
except ldm_patched.modules.model_management.InterruptProcessingException as e:
if async_task.last_stop == 'skip':
print('User skipped')