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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 = 8
load_models(models)
css = """
.model_info { text-align: center; }
.output { width=112px; height=112px; !important; }
.gallery { width=100%; min_height=768px; !important; }
"""
with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo:
with gr.Column():
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.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):
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")
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")
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 🎲", size="sm", scale=1)
clear_prompt = gr.Button(value="Clear Prompt 🗑️", size="sm", scale=1)
prompt = gr.Text(label="Prompt", lines=2, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True)
neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="", visible=False)
with gr.Accordion("Recommended Prompt", open=False):
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=[], visible=False)
negative_suffix = gr.CheckboxGroup(label="Use Negative Suffix", choices=get_negative_suffix(), value=["Common"], visible=False)
with gr.Accordion("Prompt Transformer", open=False):
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")
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", size="sm", interactive=False)
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", scale=6)
random_button = gr.Button("Random Model 🎲", scale=3)
stop_button = gr.Button('Stop', interactive=False, scale=1)
with gr.Column():
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 🗑️")
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.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, l1, l2, l3, l4: infer_fn(m, t1, t2, l1, l2, l3, l4) if (i < n) else None,
inputs=[img_i, image_num, model_name, prompt, neg_prompt, 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, l1, l2, l3, l4: infer_rand_fn(m, t1, t2, l1, l2, l3, l4) if (i < n) else None,
inputs=[img_i, image_num, model_name, prompt, neg_prompt, 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()
demo.launch()
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