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Running
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Zero
File size: 7,671 Bytes
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import time
import torch
from typing import Callable
from pathlib import Path
from dartrs.v2 import (
V2Model,
MixtralModel,
MistralModel,
compose_prompt,
LengthTag,
AspectRatioTag,
RatingTag,
IdentityTag,
)
from dartrs.dartrs import DartTokenizer
from dartrs.utils import get_generation_config
import gradio as gr
from gradio.components import Component
try:
from output import UpsamplingOutput
except:
from .output import UpsamplingOutput
V2_ALL_MODELS = {
"dart-v2-moe-sft": {
"repo": "p1atdev/dart-v2-moe-sft",
"type": "sft",
"class": MixtralModel,
},
"dart-v2-sft": {
"repo": "p1atdev/dart-v2-sft",
"type": "sft",
"class": MistralModel,
},
}
def prepare_models(model_config: dict):
model_name = model_config["repo"]
tokenizer = DartTokenizer.from_pretrained(model_name)
model = model_config["class"].from_pretrained(model_name)
return {
"tokenizer": tokenizer,
"model": model,
}
def normalize_tags(tokenizer: DartTokenizer, tags: str):
"""Just remove unk tokens."""
return ", ".join([tag for tag in tokenizer.tokenize(tags) if tag != "<|unk|>"])
@torch.no_grad()
def generate_tags(
model: V2Model,
tokenizer: DartTokenizer,
prompt: str,
ban_token_ids: list[int],
):
output = model.generate(
get_generation_config(
prompt,
tokenizer=tokenizer,
temperature=1,
top_p=0.9,
top_k=100,
max_new_tokens=256,
ban_token_ids=ban_token_ids,
),
)
return output
def _people_tag(noun: str, minimum: int = 1, maximum: int = 5):
return (
[f"1{noun}"]
+ [f"{num}{noun}s" for num in range(minimum + 1, maximum + 1)]
+ [f"{maximum+1}+{noun}s"]
)
PEOPLE_TAGS = (
_people_tag("girl") + _people_tag("boy") + _people_tag("other") + ["no humans"]
)
def gen_prompt_text(output: UpsamplingOutput):
# separate people tags (e.g. 1girl)
people_tags = []
other_general_tags = []
for tag in output.general_tags.split(","):
tag = tag.strip()
if tag in PEOPLE_TAGS:
people_tags.append(tag)
else:
other_general_tags.append(tag)
return ", ".join(
[
part.strip()
for part in [
*people_tags,
output.character_tags,
output.copyright_tags,
*other_general_tags,
output.upsampled_tags,
output.rating_tag,
]
if part.strip() != ""
]
)
def elapsed_time_format(elapsed_time: float) -> str:
return f"Elapsed: {elapsed_time:.2f} seconds"
def parse_upsampling_output(
upsampler: Callable[..., UpsamplingOutput],
):
def _parse_upsampling_output(*args) -> tuple[str, str, dict]:
output = upsampler(*args)
return (
gen_prompt_text(output),
elapsed_time_format(output.elapsed_time),
gr.update(interactive=True),
gr.update(interactive=True),
)
return _parse_upsampling_output
class V2UI:
model_name: str | None = None
model: V2Model
tokenizer: DartTokenizer
input_components: list[Component] = []
generate_btn: gr.Button
def on_generate(
self,
model_name: str,
copyright_tags: str,
character_tags: str,
general_tags: str,
rating_tag: RatingTag,
aspect_ratio_tag: AspectRatioTag,
length_tag: LengthTag,
identity_tag: IdentityTag,
ban_tags: str,
*args,
) -> UpsamplingOutput:
if self.model_name is None or self.model_name != model_name:
models = prepare_models(V2_ALL_MODELS[model_name])
self.model = models["model"]
self.tokenizer = models["tokenizer"]
self.model_name = model_name
# normalize tags
# copyright_tags = normalize_tags(self.tokenizer, copyright_tags)
# character_tags = normalize_tags(self.tokenizer, character_tags)
# general_tags = normalize_tags(self.tokenizer, general_tags)
ban_token_ids = self.tokenizer.encode(ban_tags.strip())
prompt = compose_prompt(
prompt=general_tags,
copyright=copyright_tags,
character=character_tags,
rating=rating_tag,
aspect_ratio=aspect_ratio_tag,
length=length_tag,
identity=identity_tag,
)
start = time.time()
upsampled_tags = generate_tags(
self.model,
self.tokenizer,
prompt,
ban_token_ids,
)
elapsed_time = time.time() - start
return UpsamplingOutput(
upsampled_tags=upsampled_tags,
copyright_tags=copyright_tags,
character_tags=character_tags,
general_tags=general_tags,
rating_tag=rating_tag,
aspect_ratio_tag=aspect_ratio_tag,
length_tag=length_tag,
identity_tag=identity_tag,
elapsed_time=elapsed_time,
)
def parse_upsampling_output_simple(upsampler: UpsamplingOutput):
return gen_prompt_text(upsampler)
v2 = V2UI()
def v2_upsampling_prompt(model: str = "dart-v2-moe-sft", copyright: str = "", character: str = "",
general_tags: str = "", rating: str = "nsfw", aspect_ratio: str = "square",
length: str = "very_long", identity: str = "lax", ban_tags: str = "censored"):
raw_prompt = parse_upsampling_output_simple(v2.on_generate(model, copyright, character, general_tags,
rating, aspect_ratio, length, identity, ban_tags))
return raw_prompt
def load_dict_from_csv(filename):
dict = {}
if not Path(filename).exists():
if Path('./tagger/', filename).exists(): filename = str(Path('./tagger/', filename))
else: return dict
try:
with open(filename, 'r', encoding="utf-8") as f:
lines = f.readlines()
except Exception:
print(f"Failed to open dictionary file: {filename}")
return dict
for line in lines:
parts = line.strip().split(',')
dict[parts[0]] = parts[1]
return dict
anime_series_dict = load_dict_from_csv('character_series_dict.csv')
def select_random_character(series: str, character: str):
from random import seed, randrange
seed()
character_list = list(anime_series_dict.keys())
character = character_list[randrange(len(character_list) - 1)]
series = anime_series_dict.get(character.split(",")[0].strip(), "")
return series, character
def v2_random_prompt(general_tags: str = "", copyright: str = "", character: str = "", rating: str = "nsfw",
aspect_ratio: str = "square", length: str = "very_long", identity: str = "lax",
ban_tags: str = "censored", model: str = "dart-v2-moe-sft"):
if copyright == "" and character == "":
copyright, character = select_random_character("", "")
raw_prompt = v2_upsampling_prompt(model, copyright, character, general_tags, rating,
aspect_ratio, length, identity, ban_tags)
return raw_prompt, copyright, character |