File size: 12,713 Bytes
4308686 fce6377 6223942 1b5c7d5 6223942 fce6377 1b5c7d5 6223942 4308686 6223942 1b5c7d5 4308686 fce6377 1b5c7d5 6223942 fce6377 4308686 1b5c7d5 6223942 1b5c7d5 6223942 1b5c7d5 6223942 1b5c7d5 29cc4a2 4308686 1b5c7d5 6223942 29cc4a2 1b5c7d5 6223942 29cc4a2 1b5c7d5 4308686 fce6377 1b5c7d5 fce6377 6223942 5d172b6 fce6377 5d172b6 fce6377 6223942 4308686 6223942 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
import gradio as gr
from model import models
from multit2i import (load_models, infer_fn, infer_rand_fn, save_gallery,
change_model, warm_model, get_model_info_md, loaded_models,
get_positive_prefix, get_positive_suffix, get_negative_prefix, get_negative_suffix,
get_recom_prompt_type, set_recom_prompt_preset, get_tag_type)
from tagger.tagger import (predict_tags_wd, remove_specific_prompt, convert_danbooru_to_e621_prompt,
insert_recom_prompt, compose_prompt_to_copy)
from tagger.fl2sd3longcap import predict_tags_fl2_sd3
from tagger.v2 import V2_ALL_MODELS, v2_random_prompt
from tagger.utils import (V2_ASPECT_RATIO_OPTIONS, V2_RATING_OPTIONS,
V2_LENGTH_OPTIONS, V2_IDENTITY_OPTIONS)
max_images = 6
MAX_SEED = 2**32-1
load_models(models)
css = """
.model_info { text-align: center; }
.output { width=112px; height=112px; max_width=112px; max_height=112px; !important; }
.gallery { min_width=512px; min_height=512px; max_height=1024px; !important; }
"""
with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo:
with gr.Row():
with gr.Column(scale=10):
with gr.Group():
with gr.Accordion("Prompt from Image File", open=False):
tagger_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
with gr.Accordion(label="Advanced options", open=False):
with gr.Row():
tagger_general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
tagger_character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
tagger_tag_type = gr.Radio(label="Convert tags to", info="danbooru for common, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru")
with gr.Row():
tagger_recom_prompt = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True)
tagger_keep_tags = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all")
tagger_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-SD3-Long-Captioner"], label="Algorithms", value=["Use WD Tagger"])
tagger_generate_from_image = gr.Button(value="Generate Tags from Image", variant="secondary")
with gr.Accordion("Prompt Transformer", open=False):
with gr.Row():
v2_rating = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="sfw")
v2_aspect_ratio = gr.Radio(label="Aspect ratio", info="The aspect ratio of the image.", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square", visible=False)
v2_length = gr.Radio(label="Length", info="The total length of the tags.", choices=list(V2_LENGTH_OPTIONS), value="long")
with gr.Row():
v2_identity = gr.Radio(label="Keep identity", info="How strictly to keep the identity of the character or subject. If you specify the detail of subject in the prompt, you should choose `strict`. Otherwise, choose `none` or `lax`. `none` is very creative but sometimes ignores the input prompt.", choices=list(V2_IDENTITY_OPTIONS), value="lax")
v2_ban_tags = gr.Textbox(label="Ban tags", info="Tags to ban from the output.", placeholder="alternate costumen, ...", value="censored")
v2_tag_type = gr.Radio(label="Tag Type", info="danbooru for common, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru", visible=False)
v2_model = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0])
v2_copy = gr.Button(value="Copy to clipboard", variant="secondary", size="sm", interactive=False)
with gr.Row():
v2_character = gr.Textbox(label="Character", placeholder="hatsune miku", scale=2)
v2_series = gr.Textbox(label="Series", placeholder="vocaloid", scale=2)
random_prompt = gr.Button(value="Extend Prompt 🎲", variant="secondary", size="sm", scale=1)
clear_prompt = gr.Button(value="Clear Prompt 🗑️", variant="secondary", size="sm", scale=1)
prompt = gr.Text(label="Prompt", lines=2, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True)
with gr.Accordion("Advanced options", open=False):
neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="")
with gr.Row():
width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
with gr.Row():
steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
recom_prompt_preset = gr.Radio(label="Set Presets", choices=get_recom_prompt_type(), value="Common")
with gr.Row():
positive_prefix = gr.CheckboxGroup(label="Use Positive Prefix", choices=get_positive_prefix(), value=[])
positive_suffix = gr.CheckboxGroup(label="Use Positive Suffix", choices=get_positive_suffix(), value=["Common"])
negative_prefix = gr.CheckboxGroup(label="Use Negative Prefix", choices=get_negative_prefix(), value=[])
negative_suffix = gr.CheckboxGroup(label="Use Negative Suffix", choices=get_negative_suffix(), value=["Common"])
image_num = gr.Slider(label="Number of images", minimum=1, maximum=max_images, value=1, step=1, interactive=True, scale=1)
with gr.Row():
run_button = gr.Button("Generate Image", variant="primary", scale=6)
random_button = gr.Button("Random Model 🎲", variant="secondary", scale=3)
stop_button = gr.Button('Stop', variant="stop", interactive=False, scale=1)
with gr.Group():
model_name = gr.Dropdown(label="Select Model", choices=list(loaded_models.keys()), value=list(loaded_models.keys())[0], allow_custom_value=True)
model_info = gr.Markdown(value=get_model_info_md(list(loaded_models.keys())[0]), elem_classes="model_info")
with gr.Column(scale=10):
with gr.Group():
with gr.Row():
output = [gr.Image(label='', elem_classes="output", type="filepath", format="png",
show_download_button=True, show_share_button=False, show_label=False,
interactive=False, min_width=80, visible=True) for _ in range(max_images)]
with gr.Group():
results = gr.Gallery(label="Gallery", elem_classes="gallery", interactive=False, show_download_button=True, show_share_button=False,
container=True, format="png", object_fit="cover", columns=2, rows=2)
image_files = gr.Files(label="Download", interactive=False)
clear_results = gr.Button("Clear Gallery / Download 🗑️", variant="secondary")
with gr.Column():
examples = gr.Examples(
examples = [
["souryuu asuka langley, 1girl, neon genesis evangelion, plugsuit, pilot suit, red bodysuit, sitting, crossing legs, black eye patch, cat hat, throne, symmetrical, looking down, from bottom, looking at viewer, outdoors"],
["sailor moon, magical girl transformation, sparkles and ribbons, soft pastel colors, crescent moon motif, starry night sky background, shoujo manga style"],
["kafuu chino, 1girl, solo"],
["1girl"],
["beautiful sunset"],
],
inputs=[prompt],
)
gr.Markdown(
f"""This demo was created in reference to the following demos.<br>
[Nymbo/Flood](https://huggingface.co/spaces/Nymbo/Flood),
[Yntec/ToyWorldXL](https://huggingface.co/spaces/Yntec/ToyWorldXL),
[Yntec/Diffusion80XX](https://huggingface.co/spaces/Yntec/Diffusion80XX).
"""
)
gr.DuplicateButton(value="Duplicate Space")
gr.Markdown(f"Just a few edits to *model.py* are all it takes to complete your own collection.")
gr.on(triggers=[run_button.click, prompt.submit, random_button.click], fn=lambda: gr.update(interactive=True), inputs=None, outputs=stop_button, show_api=False)
model_name.change(change_model, [model_name], [model_info], queue=False, show_api=False)\
.success(warm_model, [model_name], None, queue=True, show_api=False)
for i, o in enumerate(output):
img_i = gr.Number(i, visible=False)
image_num.change(lambda i, n: gr.update(visible = (i < n)), [img_i, image_num], o, show_api=False)
gen_event = gr.on(triggers=[run_button.click, prompt.submit],
fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None,
inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
positive_prefix, positive_suffix, negative_prefix, negative_suffix],
outputs=[o], queue=True, show_api=False)
gen_event2 = gr.on(triggers=[random_button.click],
fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_rand_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None,
inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
positive_prefix, positive_suffix, negative_prefix, negative_suffix],
outputs=[o], queue=True, show_api=False)
o.change(save_gallery, [o, results], [results, image_files], show_api=False)
stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event, gen_event2], show_api=False)
clear_prompt.click(lambda: None, None, [prompt], queue=False, show_api=False)
clear_results.click(lambda: (None, None), None, [results, image_files], queue=False, show_api=False)
recom_prompt_preset.change(set_recom_prompt_preset, [recom_prompt_preset],
[positive_prefix, positive_suffix, negative_prefix, negative_suffix], queue=False, show_api=False)
random_prompt.click(
v2_random_prompt, [prompt, v2_series, v2_character, v2_rating, v2_aspect_ratio, v2_length,
v2_identity, v2_ban_tags, v2_model], [prompt, v2_series, v2_character], show_api=False,
).success(get_tag_type, [positive_prefix, positive_suffix, negative_prefix, negative_suffix], [v2_tag_type], queue=False, show_api=False
).success(convert_danbooru_to_e621_prompt, [prompt, v2_tag_type], [prompt], queue=False, show_api=False)
tagger_generate_from_image.click(lambda: ("", "", ""), None, [v2_series, v2_character, prompt], queue=False, show_api=False,
).success(
predict_tags_wd,
[tagger_image, prompt, tagger_algorithms, tagger_general_threshold, tagger_character_threshold],
[v2_series, v2_character, prompt, v2_copy],
show_api=False,
).success(predict_tags_fl2_sd3, [tagger_image, prompt, tagger_algorithms], [prompt], show_api=False,
).success(remove_specific_prompt, [prompt, tagger_keep_tags], [prompt], queue=False, show_api=False,
).success(convert_danbooru_to_e621_prompt, [prompt, tagger_tag_type], [prompt], queue=False, show_api=False,
).success(insert_recom_prompt, [prompt, neg_prompt, tagger_recom_prompt], [prompt, neg_prompt], queue=False, show_api=False,
).success(compose_prompt_to_copy, [v2_character, v2_series, prompt], [prompt], queue=False, show_api=False)
demo.queue(default_concurrency_limit=200, max_size=200)
demo.launch(max_threads=400)
|