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"""
Image meta schema
"""
from typing import List
from fooocus_version import version
from pydantic import BaseModel
class ImageMeta(BaseModel):
"""
Image meta data model
"""
metadata_scheme: str = "fooocus"
base_model: str
base_model_hash: str
prompt: str
full_prompt: List[str]
prompt_expansion: str
negative_prompt: str
full_negative_prompt: List[str]
performance: str
style: str
refiner_model: str = "None"
refiner_switch: float = 0.5
loras: List[list]
resolution: str
sampler: str = "dpmpp_2m_sde_gpu"
scheduler: str = "karras"
seed: str
adm_guidance: str
guidance_scale: float
sharpness: float
steps: int
vae_name: str
version: str = version
def __repr__(self):
return ""
def loras_parser(loras: list) -> list:
"""
Parse lora list
"""
return [
[
lora[0].rsplit('.', maxsplit=1)[:1][0],
lora[1],
"hash_not_calculated",
] for lora in loras if lora[0] != 'None' and lora[0] is not None]
def image_parse(
async_tak: object,
task: dict
) -> dict | str:
"""
Parse image meta data
Generate meta data for image from task and async task object
Args:
async_tak: async task obj
task: task obj
Returns:
dict: image meta data
"""
req_param = async_tak.req_param
meta = ImageMeta(
metadata_scheme=req_param.meta_scheme,
base_model=req_param.base_model_name.rsplit('.', maxsplit=1)[:1][0],
base_model_hash='',
prompt=req_param.prompt,
full_prompt=task['positive'],
prompt_expansion=task['expansion'],
negative_prompt=req_param.negative_prompt,
full_negative_prompt=task['negative'],
performance=req_param.performance_selection,
style=str(req_param.style_selections),
refiner_model=req_param.refiner_model_name,
refiner_switch=req_param.refiner_switch,
loras=loras_parser(req_param.loras),
resolution=str(tuple([int(n) for n in req_param.aspect_ratios_selection.split('*')])),
sampler=req_param.advanced_params.sampler_name,
scheduler=req_param.advanced_params.scheduler_name,
seed=str(task['task_seed']),
adm_guidance=str((
req_param.advanced_params.adm_scaler_positive,
req_param.advanced_params.adm_scaler_negative,
req_param.advanced_params.adm_scaler_end)),
guidance_scale=req_param.guidance_scale,
sharpness=req_param.sharpness,
steps=-1,
vae_name=req_param.advanced_params.vae_name,
version=version
)
if meta.metadata_scheme not in ["fooocus", "a111"]:
meta.metadata_scheme = "fooocus"
if meta.metadata_scheme == "fooocus":
meta_dict = meta.model_dump()
for i, lora in enumerate(meta.loras):
attr_name = f"lora_combined_{i+1}"
lr = [str(x) for x in lora]
meta_dict[attr_name] = f"{lr[0]} : {lr[1]}"
else:
meta_dict = meta.model_dump()
return meta_dict
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