diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/deepbooru.py | 36 | ||||
| -rw-r--r-- | modules/shared.py | 6 |
2 files changed, 31 insertions, 11 deletions
diff --git a/modules/deepbooru.py b/modules/deepbooru.py index ebdba5e0..e31e92c0 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -3,31 +3,32 @@ from concurrent.futures import ProcessPoolExecutor import multiprocessing import time - -def get_deepbooru_tags(pil_image, threshold=0.5): +def get_deepbooru_tags(pil_image): """ This method is for running only one image at a time for simple use. Used to the img2img interrogate. """ from modules import shared # prevents circular reference - create_deepbooru_process(threshold) + create_deepbooru_process(shared.opts.deepbooru_threshold, shared.opts.deepbooru_sort_alpha) shared.deepbooru_process_return["value"] = -1 shared.deepbooru_process_queue.put(pil_image) while shared.deepbooru_process_return["value"] == -1: time.sleep(0.2) + tags = shared.deepbooru_process_return["value"] release_process() + return tags -def deepbooru_process(queue, deepbooru_process_return, threshold): +def deepbooru_process(queue, deepbooru_process_return, threshold, alpha_sort): model, tags = get_deepbooru_tags_model() while True: # while process is running, keep monitoring queue for new image pil_image = queue.get() if pil_image == "QUIT": break else: - deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold) + deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort) -def create_deepbooru_process(threshold=0.5): +def create_deepbooru_process(threshold, alpha_sort): """ Creates deepbooru process. A queue is created to send images into the process. This enables multiple images to be processed in a row without reloading the model or creating a new process. To return the data, a shared @@ -40,7 +41,7 @@ def create_deepbooru_process(threshold=0.5): shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue() shared.deepbooru_process_return = shared.deepbooru_process_manager.dict() shared.deepbooru_process_return["value"] = -1 - shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold)) + shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, alpha_sort)) shared.deepbooru_process.start() @@ -80,7 +81,7 @@ def get_deepbooru_tags_model(): return model, tags -def get_deepbooru_tags_from_model(model, tags, pil_image, threshold=0.5): +def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort): import deepdanbooru as dd import tensorflow as tf import numpy as np @@ -105,15 +106,28 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold=0.5): for i, tag in enumerate(tags): result_dict[tag] = y[i] - result_tags_out = [] + + unsorted_tags_in_theshold = [] result_tags_print = [] for tag in tags: if result_dict[tag] >= threshold: if tag.startswith("rating:"): continue - result_tags_out.append(tag) + unsorted_tags_in_theshold.append((result_dict[tag], tag)) result_tags_print.append(f'{result_dict[tag]} {tag}') + # sort tags + result_tags_out = [] + sort_ndx = 0 + print(alpha_sort) + if alpha_sort: + sort_ndx = 1 + + # sort by reverse by likelihood and normal for alpha + unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort)) + for weight, tag in unsorted_tags_in_theshold: + result_tags_out.append(tag) + print('\n'.join(sorted(result_tags_print, reverse=True))) - return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
\ No newline at end of file + return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ') diff --git a/modules/shared.py b/modules/shared.py index 1995a99a..2e307809 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -261,6 +261,12 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
}))
+if cmd_opts.deepdanbooru:
+ options_templates.update(options_section(('deepbooru-params', "DeepBooru parameters"), {
+ "deepbooru_sort_alpha": OptionInfo(True, "Sort Alphabetical", gr.Checkbox),
+ 'deepbooru_threshold': OptionInfo(0.5, "Threshold", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ }))
+
class Options:
data = None
|