import glob
import io
import json
import logging
import os
import shutil
from dataclasses import dataclass
from textwrap import dedent
from typing import List, Union, Optional
import yaml
from PIL.PngImagePlugin import PngInfo
from imgutils.detect import detect_censors
try:
from yaml import CLoader as Loader, CDumper as Dumper
except ImportError:
from yaml import Loader, Dumper
from PIL import Image
from hbutils.system import TemporaryDirectory
from hcpdiff import Visualizer
from hcpdiff.utils import load_config_with_cli
from ..utils import data_to_cli_args
_DEFAULT_INFER_CFG_FILE = 'cfgs/infer/text2img_anime_lora.yaml'
_DEFAULT_INFER_MODEL = 'LittleApple-fp16/SpiritForeseerMix'
def sample_method_to_config(method):
if method == 'DPM++ SDE Karras':
return {
'_target_': 'diffusers.DPMSolverSDEScheduler',
'beta_start': 0.00085,
'beta_end': 0.012,
'beta_schedule': 'scaled_linear',
'use_karras_sigmas': True,
}
elif method == 'DPM++ 2M Karras':
return {
'_target_': 'diffusers.DPMSolverMultistepScheduler',
'beta_start': 0.00085,
'beta_end': 0.012,
'algorithm_type': 'dpmsolver++',
'beta_schedule': 'scaled_linear',
'use_karras_sigmas': True
}
elif method == 'Euler a':
return {
'_target_': 'diffusers.EulerAncestralDiscreteScheduler',
'beta_start': 0.00085,
'beta_end': 0.012,
'beta_schedule': 'scaled_linear',
}
else:
raise ValueError(f'Unknown sample method - {method!r}.')
def draw_images(
workdir: str, prompts: Union[str, List[str]], neg_prompts: Union[str, List[str]] = None,
seeds: Union[int, List[str]] = None, emb_name: str = None, save_cfg: bool = True,
model_steps: int = 1000, n_repeats: int = 2, pretrained_model: str = _DEFAULT_INFER_MODEL,
width: int = 512, height: int = 768, gscale: float = 8, infer_steps: int = 30,
lora_alpha: float = 0.85, output_dir: str = 'output', cfg_file: str = _DEFAULT_INFER_CFG_FILE,
clip_skip: int = 2, sample_method: str = 'DPM++ 2M Karras',
):
emb_name = emb_name or os.path.basename(workdir)
with TemporaryDirectory() as emb_dir:
src_pt_files = glob.glob(os.path.join(workdir, 'ckpts', f'*-{model_steps}.pt'))
if not src_pt_files:
raise FileNotFoundError(f'Embedding not found for step {model_steps}.')
src_pt_file = src_pt_files[0]
shutil.copyfile(src_pt_file, os.path.join(emb_dir, f'{emb_name}.pt'))
cli_args = data_to_cli_args({
'pretrained_model': pretrained_model,
'N_repeats': n_repeats,
'vae_optimize': {
'tiling': False,
},
'clip_skip': clip_skip - 1,
'bs': 1,
'num': 1,
'infer_args': {
'width': width,
'height': height,
'guidance_scale': gscale,
'num_inference_steps': infer_steps,
},
'exp_dir': workdir,
'model_steps': model_steps,
'emb_dir': emb_dir,
'output_dir': output_dir,
'merge': {
'alpha': lora_alpha,
},
'new_components': {
'scheduler': sample_method_to_config(sample_method),
'vae': {
'_target_': 'diffusers.AutoencoderKL.from_pretrained',
'pretrained_model_name_or_path': 'deepghs/animefull-latest', # path to vae model
'subfolder': 'vae',
}
}
})
logging.info(f'Infer based on {cfg_file!r}, with {cli_args!r}')
cfgs = load_config_with_cli(cfg_file, args_list=cli_args) # skip --cfg
N = None
if isinstance(prompts, list):
N = len(prompts)
if isinstance(neg_prompts, list):
if N is not None and len(neg_prompts) != N:
raise ValueError(f'Number of prompts ({len(prompts)}) and neg_prompts ({len(neg_prompts)}) not match.')
N = len(neg_prompts)
if isinstance(seeds, list):
if N is not None and len(seeds) != N:
raise ValueError(f'Number of both prompts ({N}) and seed ({len(seeds)}) not match.')
N = len(seeds)
if N is None:
N = 1
if not isinstance(prompts, list):
prompts = [prompts] * N
if not isinstance(neg_prompts, list):
neg_prompts = [neg_prompts] * N
if not isinstance(seeds, list):
seeds = [seeds] * N
viser = Visualizer(cfgs)
viser.vis_to_dir(prompt=prompts, negative_prompt=neg_prompts, seeds=seeds,
save_cfg=save_cfg, **cfgs.infer_args)
@dataclass
class Drawing:
name: str
prompt: str
neg_prompt: str
seed: int
sfw: bool
width: int
height: int
gscale: float
steps: int
image: Image.Image
sample_method: str
clip_skip: int
model: str
model_hash: Optional[str] = None
@property
def preview_info(self):
return dedent(f"""
Prompt: {self.prompt}
Neg Prompt: {self.neg_prompt}
Width: {self.width}
Height: {self.height}
Guidance Scale: {self.gscale}
Sample Method: {self.sample_method}
Infer Steps: {self.steps}
Clip Skip: {self.clip_skip}
Seed: {self.seed}
Model: {self.model}
Safe For Work: {"yes" if self.sfw else "no"}
""").lstrip()
@property
def pnginfo_text(self) -> str:
with io.StringIO() as sf:
print(self.prompt, file=sf)
print(f'Negative prompt: {self.neg_prompt}', file=sf)
if self.model_hash:
print(f'Steps: {self.steps}, Sampler: {self.sample_method}, '
f'CFG scale: {self.gscale}, Seed: {self.seed}, Size: {self.width}x{self.height}, '
f'Model hash: {self.model_hash.lower()}, Model: {self.model}, '
f'Clip skip: {self.clip_skip}', file=sf)
else:
print(f'Steps: {self.steps}, Sampler: {self.sample_method}, '
f'CFG scale: {self.gscale}, Seed: {self.seed}, Size: {self.width}x{self.height}, '
f'Model: {self.model}, '
f'Clip skip: {self.clip_skip}', file=sf)
return sf.getvalue()
@property
def pnginfo(self) -> PngInfo:
info = PngInfo()
info.add_text('parameters', self.pnginfo_text)
return info
_N_MAX_DRAW = 20
def draw_with_workdir(
workdir: str, emb_name: str = None, save_cfg: bool = True,
model_steps: int = 1000, n_repeats: int = 2, pretrained_model: str = _DEFAULT_INFER_MODEL,
width: int = 512, height: int = 768, gscale: float = 8, infer_steps: int = 30,
lora_alpha: float = 0.85, output_dir: str = None, cfg_file: str = _DEFAULT_INFER_CFG_FILE,
clip_skip: int = 2, sample_method: str = 'DPM++ 2M Karras', model_hash: Optional[str] = None,
):
n_pnames, n_prompts, n_neg_prompts, n_seeds, n_sfws = [], [], [], [], []
for jfile in glob.glob(os.path.join(workdir, 'rtags', '*.json')):
with open(jfile, 'r', encoding='utf-8') as f:
data = json.load(f)
n_pnames.append(data['name'])
n_prompts.append(data['prompt'])
n_neg_prompts.append(data['neg_prompt'])
n_seeds.append(data['seed'])
n_sfws.append(data['sfw'])
n_total = len(n_pnames)
retval = []
for x in range(0, n_total, _N_MAX_DRAW):
pnames, prompts, neg_prompts, seeds, sfws = \
n_pnames[x:x + _N_MAX_DRAW], n_prompts[x:x + _N_MAX_DRAW], n_neg_prompts[x:x + _N_MAX_DRAW], \
n_seeds[x:x + _N_MAX_DRAW], n_sfws[x:x + _N_MAX_DRAW]
with TemporaryDirectory() as td:
_tmp_output_dir = output_dir or td
draw_images(
workdir, prompts, neg_prompts, seeds,
emb_name, save_cfg, model_steps, n_repeats, pretrained_model,
width, height, gscale, infer_steps, lora_alpha, _tmp_output_dir, cfg_file,
clip_skip, sample_method,
)
for i, (pname, prompt, neg_prompt, seed, sfw) in \
enumerate(zip(pnames, prompts, neg_prompts, seeds, sfws), start=1):
img_file = glob.glob(os.path.join(_tmp_output_dir, f'{i}-*.png'))[0]
yaml_file = glob.glob(os.path.join(_tmp_output_dir, f'{i}-*.yaml'))[0]
with open(yaml_file, 'r', encoding='utf-8') as f:
seed = yaml.load(f, Loader)['seed']
img = Image.open(img_file)
img.load()
retval.append(Drawing(
pname, prompt, neg_prompt, seed,
sfw=sfw and len(detect_censors(img, conf_threshold=0.45)) == 0,
width=width, height=height, gscale=gscale, steps=infer_steps,
image=img, sample_method=sample_method, clip_skip=clip_skip,
model=pretrained_model, model_hash=model_hash,
))
return retval