feat: add support for lora inline prompt references (#2323)
* 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
GitHub
parent
3a55e7e391
commit
3bae73e23e
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@ -0,0 +1,11 @@
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## Running unit tests
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Native python:
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```
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python -m unittest tests/
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```
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Embedded python (Windows zip file installation method):
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```
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..\python_embeded\python.exe -m unittest
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```
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@ -4,6 +4,7 @@ from modules.patch import PatchSettings, patch_settings, patch_all
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patch_all()
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class AsyncTask:
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def __init__(self, args):
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self.args = args
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@ -44,11 +45,12 @@ def worker():
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import args_manager
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from extras.censor import censor_batch, censor_single
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from modules.sdxl_styles import apply_style, get_random_style, apply_wildcards, fooocus_expansion, apply_arrays, random_style_name
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from modules.sdxl_styles import apply_style, get_random_style, fooocus_expansion, apply_arrays, random_style_name
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from modules.private_logger import log
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from extras.expansion import safe_str
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from modules.util import remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil, \
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get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix, get_enabled_loras
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from modules.util import (remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil,
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get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix, get_enabled_loras,
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parse_lora_references_from_prompt, apply_wildcards)
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from modules.upscaler import perform_upscale
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from modules.flags import Performance
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from modules.meta_parser import get_metadata_parser, MetadataScheme
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@ -69,7 +71,8 @@ def worker():
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print(f'[Fooocus] {text}')
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async_task.yields.append(['preview', (number, text, None)])
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def yield_result(async_task, imgs, black_out_nsfw, censor=True, do_not_show_finished_images=False, progressbar_index=13):
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def yield_result(async_task, imgs, black_out_nsfw, censor=True, do_not_show_finished_images=False,
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progressbar_index=13):
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if not isinstance(imgs, list):
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imgs = [imgs]
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@ -152,7 +155,8 @@ def worker():
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base_model_name = args.pop()
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refiner_model_name = args.pop()
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refiner_switch = args.pop()
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loras = get_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop())] for _ in range(modules.config.default_max_lora_number)])
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loras = get_enabled_loras([(bool(args.pop()), str(args.pop()), float(args.pop())) for _ in
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range(modules.config.default_max_lora_number)])
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input_image_checkbox = args.pop()
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current_tab = args.pop()
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uov_method = args.pop()
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@ -202,7 +206,8 @@ def worker():
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inpaint_erode_or_dilate = args.pop()
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save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False
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metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
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metadata_scheme = MetadataScheme(
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args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
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cn_tasks = {x: [] for x in flags.ip_list}
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for _ in range(flags.controlnet_image_count):
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@ -433,6 +438,9 @@ def worker():
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extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []
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progressbar(async_task, 3, 'Loading models ...')
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loras = parse_lora_references_from_prompt(prompt, loras, modules.config.default_max_lora_number)
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pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
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loras=loras, base_model_additional_loras=base_model_additional_loras,
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use_synthetic_refiner=use_synthetic_refiner, vae_name=vae_name)
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@ -450,8 +458,10 @@ def worker():
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task_prompt = apply_wildcards(prompt, task_rng, i, read_wildcards_in_order)
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task_prompt = apply_arrays(task_prompt, i)
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task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, read_wildcards_in_order)
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task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_positive_prompts]
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task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_negative_prompts]
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task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in
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extra_positive_prompts]
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task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in
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extra_negative_prompts]
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positive_basic_workloads = []
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negative_basic_workloads = []
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@ -652,7 +662,8 @@ def worker():
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)
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if debugging_inpaint_preprocessor:
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yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), black_out_nsfw, do_not_show_finished_images=True)
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yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), black_out_nsfw,
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do_not_show_finished_images=True)
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return
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progressbar(async_task, 13, 'VAE Inpaint encoding ...')
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@ -807,7 +818,8 @@ def worker():
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done_steps = current_task_id * steps + step
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async_task.yields.append(['preview', (
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int(15.0 + 85.0 * float(done_steps) / float(all_steps)),
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f'Step {step}/{total_steps} in the {current_task_id + 1}{ordinal_suffix(current_task_id + 1)} Sampling', y)])
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f'Step {step}/{total_steps} in the {current_task_id + 1}{ordinal_suffix(current_task_id + 1)} Sampling',
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y)])
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for current_task_id, task in enumerate(tasks):
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execution_start_time = time.perf_counter()
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@ -862,7 +874,8 @@ def worker():
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d = [('Prompt', 'prompt', task['log_positive_prompt']),
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('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
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('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
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('Styles', 'styles', str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])),
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('Styles', 'styles',
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str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])),
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('Performance', 'performance', performance_selection.value)]
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if performance_selection.steps() != steps:
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@ -885,7 +898,8 @@ def worker():
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if refiner_swap_method != flags.refiner_swap_method:
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d.append(('Refiner Swap Method', 'refiner_swap_method', refiner_swap_method))
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if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr:
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d.append(('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
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d.append(
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('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
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d.append(('Sampler', 'sampler', sampler_name))
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d.append(('Scheduler', 'scheduler', scheduler_name))
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@ -905,11 +919,13 @@ def worker():
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metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
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task['log_negative_prompt'], task['negative'],
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steps, base_model_name, refiner_model_name, loras, vae_name)
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d.append(('Metadata Scheme', 'metadata_scheme', metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
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d.append(('Metadata Scheme', 'metadata_scheme',
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metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
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d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
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img_paths.append(log(x, d, metadata_parser, output_format, task))
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yield_result(async_task, img_paths, black_out_nsfw, False, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
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yield_result(async_task, img_paths, black_out_nsfw, False,
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do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
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except ldm_patched.modules.model_management.InterruptProcessingException as e:
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if async_task.last_stop == 'skip':
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print('User skipped')
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@ -8,7 +8,8 @@ import modules.flags
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import modules.sdxl_styles
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from modules.model_loader import load_file_from_url
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from modules.util import get_files_from_folder, makedirs_with_log
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from modules.util import makedirs_with_log
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from modules.extra_utils import get_files_from_folder
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from modules.flags import OutputFormat, Performance, MetadataScheme
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@ -20,7 +21,7 @@ def get_config_path(key, default_value):
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else:
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return os.path.abspath(default_value)
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wildcards_max_bfs_depth = 64
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config_path = get_config_path('config_path', "./config.txt")
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config_example_path = get_config_path('config_example_path', "config_modification_tutorial.txt")
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config_dict = {}
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@ -0,0 +1,20 @@
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import os
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def get_files_from_folder(folder_path, extensions=None, name_filter=None):
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if not os.path.isdir(folder_path):
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raise ValueError("Folder path is not a valid directory.")
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filenames = []
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for root, _, files in os.walk(folder_path, topdown=False):
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relative_path = os.path.relpath(root, folder_path)
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if relative_path == ".":
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relative_path = ""
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for filename in sorted(files, key=lambda s: s.casefold()):
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_, file_extension = os.path.splitext(filename)
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if (extensions is None or file_extension.lower() in extensions) and (name_filter is None or name_filter in _):
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path = os.path.join(relative_path, filename)
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filenames.append(path)
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return filenames
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@ -2,14 +2,12 @@ import os
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import re
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import json
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import math
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import modules.config
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from modules.util import get_files_from_folder
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from modules.extra_utils import get_files_from_folder
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from random import Random
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# cannot use modules.config - validators causing circular imports
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styles_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../sdxl_styles/'))
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wildcards_max_bfs_depth = 64
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def normalize_key(k):
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@ -25,7 +23,6 @@ def normalize_key(k):
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styles = {}
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styles_files = get_files_from_folder(styles_path, ['.json'])
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for x in ['sdxl_styles_fooocus.json',
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@ -65,34 +62,7 @@ def apply_style(style, positive):
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return p.replace('{prompt}', positive).splitlines(), n.splitlines()
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def apply_wildcards(wildcard_text, rng, i, read_wildcards_in_order):
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for _ in range(wildcards_max_bfs_depth):
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placeholders = re.findall(r'__([\w-]+)__', wildcard_text)
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if len(placeholders) == 0:
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return wildcard_text
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print(f'[Wildcards] processing: {wildcard_text}')
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for placeholder in placeholders:
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try:
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matches = [x for x in modules.config.wildcard_filenames if os.path.splitext(os.path.basename(x))[0] == placeholder]
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words = open(os.path.join(modules.config.path_wildcards, matches[0]), encoding='utf-8').read().splitlines()
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words = [x for x in words if x != '']
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assert len(words) > 0
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if read_wildcards_in_order:
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wildcard_text = wildcard_text.replace(f'__{placeholder}__', words[i % len(words)], 1)
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else:
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wildcard_text = wildcard_text.replace(f'__{placeholder}__', rng.choice(words), 1)
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except:
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print(f'[Wildcards] Warning: {placeholder}.txt missing or empty. '
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f'Using "{placeholder}" as a normal word.')
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wildcard_text = wildcard_text.replace(f'__{placeholder}__', placeholder)
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print(f'[Wildcards] {wildcard_text}')
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print(f'[Wildcards] BFS stack overflow. Current text: {wildcard_text}')
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return wildcard_text
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def get_words(arrays, totalMult, index):
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def get_words(arrays, total_mult, index):
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if len(arrays) == 1:
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return [arrays[0].split(',')[index]]
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else:
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@ -101,7 +71,7 @@ def get_words(arrays, totalMult, index):
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index -= index % len(words)
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index /= len(words)
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index = math.floor(index)
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return [word] + get_words(arrays[1:], math.floor(totalMult/len(words)), index)
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return [word] + get_words(arrays[1:], math.floor(total_mult / len(words)), index)
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def apply_arrays(text, index):
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@ -1,11 +1,12 @@
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import typing
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import numpy as np
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import datetime
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import random
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import math
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import os
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import cv2
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import re
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from typing import List, Tuple, AnyStr, NamedTuple
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import json
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import hashlib
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@ -14,8 +15,16 @@ from PIL import Image
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import modules.sdxl_styles
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LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
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# Regexp compiled once. Matches entries with the following pattern:
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# <lora:some_lora:1>
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# <lora:aNotherLora:-1.6>
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LORAS_PROMPT_PATTERN = re.compile(r".* <lora : ([^:]+) : ([+-]? (?: (?:\d+ (?:\.\d*)?) | (?:\.\d+)))> .*", re.X)
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HASH_SHA256_LENGTH = 10
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def erode_or_dilate(x, k):
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k = int(k)
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if k > 0:
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@ -163,25 +172,6 @@ def generate_temp_filename(folder='./outputs/', extension='png'):
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return date_string, os.path.abspath(result), filename
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def get_files_from_folder(folder_path, extensions=None, name_filter=None):
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if not os.path.isdir(folder_path):
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raise ValueError("Folder path is not a valid directory.")
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filenames = []
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for root, dirs, files in os.walk(folder_path, topdown=False):
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relative_path = os.path.relpath(root, folder_path)
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if relative_path == ".":
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relative_path = ""
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for filename in sorted(files, key=lambda s: s.casefold()):
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_, file_extension = os.path.splitext(filename)
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if (extensions is None or file_extension.lower() in extensions) and (name_filter is None or name_filter in _):
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path = os.path.join(relative_path, filename)
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filenames.append(path)
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return filenames
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def sha256(filename, use_addnet_hash=False, length=HASH_SHA256_LENGTH):
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print(f"Calculating sha256 for {filename}: ", end='')
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if use_addnet_hash:
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@ -355,7 +345,7 @@ def extract_styles_from_prompt(prompt, negative_prompt):
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return list(reversed(extracted)), real_prompt, negative_prompt
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class PromptStyle(typing.NamedTuple):
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class PromptStyle(NamedTuple):
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name: str
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prompt: str
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negative_prompt: str
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@ -394,4 +384,47 @@ def makedirs_with_log(path):
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def get_enabled_loras(loras: list) -> list:
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return [[lora[1], lora[2]] for lora in loras if lora[0]]
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return [(lora[1], lora[2]) for lora in loras if lora[0]]
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def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, float]], loras_limit: int = 5) -> List[Tuple[AnyStr, float]]:
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new_loras = []
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updated_loras = []
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for token in prompt.split(","):
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m = LORAS_PROMPT_PATTERN.match(token)
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if m:
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new_loras.append((f"{m.group(1)}.safetensors", float(m.group(2))))
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for lora in loras + new_loras:
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if lora[0] != "None":
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updated_loras.append(lora)
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return updated_loras[:loras_limit]
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def apply_wildcards(wildcard_text, rng, i, read_wildcards_in_order) -> str:
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for _ in range(modules.config.wildcards_max_bfs_depth):
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placeholders = re.findall(r'__([\w-]+)__', wildcard_text)
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if len(placeholders) == 0:
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return wildcard_text
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print(f'[Wildcards] processing: {wildcard_text}')
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for placeholder in placeholders:
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try:
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matches = [x for x in modules.config.wildcard_filenames if os.path.splitext(os.path.basename(x))[0] == placeholder]
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words = open(os.path.join(modules.config.path_wildcards, matches[0]), encoding='utf-8').read().splitlines()
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words = [x for x in words if x != '']
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assert len(words) > 0
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if read_wildcards_in_order:
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wildcard_text = wildcard_text.replace(f'__{placeholder}__', words[i % len(words)], 1)
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else:
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wildcard_text = wildcard_text.replace(f'__{placeholder}__', rng.choice(words), 1)
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except:
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print(f'[Wildcards] Warning: {placeholder}.txt missing or empty. '
|
||||
f'Using "{placeholder}" as a normal word.')
|
||||
wildcard_text = wildcard_text.replace(f'__{placeholder}__', placeholder)
|
||||
print(f'[Wildcards] {wildcard_text}')
|
||||
|
||||
print(f'[Wildcards] BFS stack overflow. Current text: {wildcard_text}')
|
||||
return wildcard_text
|
||||
|
|
|
|||
|
|
@ -0,0 +1,4 @@
|
|||
import sys
|
||||
import pathlib
|
||||
|
||||
sys.path.append(pathlib.Path(f'{__file__}/../modules').parent.resolve())
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
import unittest
|
||||
|
||||
from modules import util
|
||||
|
||||
|
||||
class TestUtils(unittest.TestCase):
|
||||
def test_can_parse_tokens_with_lora(self):
|
||||
test_cases = [
|
||||
{
|
||||
"input": ("some prompt, very cool, <lora:hey-lora:0.4>, cool <lora:you-lora:0.2>", [], 5),
|
||||
"output": [("hey-lora.safetensors", 0.4), ("you-lora.safetensors", 0.2)],
|
||||
},
|
||||
# Test can not exceed limit
|
||||
{
|
||||
"input": ("some prompt, very cool, <lora:hey-lora:0.4>, cool <lora:you-lora:0.2>", [], 1),
|
||||
"output": [("hey-lora.safetensors", 0.4)],
|
||||
},
|
||||
# test Loras from UI take precedence over prompt
|
||||
{
|
||||
"input": (
|
||||
"some prompt, very cool, <lora:l1:0.4>, <lora:l2:-0.2>, <lora:l3:0.3>, <lora:l4:0.5>, <lora:l6:0.24>, <lora:l7:0.1>",
|
||||
[("hey-lora.safetensors", 0.4)],
|
||||
5,
|
||||
),
|
||||
"output": [
|
||||
("hey-lora.safetensors", 0.4),
|
||||
("l1.safetensors", 0.4),
|
||||
("l2.safetensors", -0.2),
|
||||
("l3.safetensors", 0.3),
|
||||
("l4.safetensors", 0.5),
|
||||
],
|
||||
},
|
||||
# Test lora specification not separated by comma are ignored, only latest specified is used
|
||||
{
|
||||
"input": ("some prompt, very cool, <lora:hey-lora:0.4><lora:you-lora:0.2>", [], 3),
|
||||
"output": [("you-lora.safetensors", 0.2)],
|
||||
},
|
||||
{
|
||||
"input": ("<lora:foo:1..2>, <lora:bar:.>, <lora:baz:+> and <lora:quux:>", [], 6),
|
||||
"output": []
|
||||
}
|
||||
]
|
||||
|
||||
for test in test_cases:
|
||||
prompt, loras, loras_limit = test["input"]
|
||||
expected = test["output"]
|
||||
actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit)
|
||||
self.assertEqual(expected, actual)
|
||||
Loading…
Reference in New Issue