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from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List
@dataclass
class RunConfig:
# Guiding text prompt
prompt: str = "<task-prompt>"
# Whether to use Stable Diffusion v2.1
sd_2_1: bool = False
# Which token indices to alter with attend-and-excite
token_indices: List[int] = field(default_factory=lambda: [2,5])
# Which random seeds to use when generating
seeds: List[int] = field(default_factory=lambda: [42])
# Path to save all outputs to
output_path: Path = Path('./outputs')
# Number of denoising steps
n_inference_steps: int = 50
# Text guidance scale
guidance_scale: float = 7.5
# Number of denoising steps to apply attend-and-excite
max_iter_to_alter: int = 25
# Resolution of UNet to compute attention maps over
attention_res: int = 16
# Whether to run standard SD or attend-and-excite
run_standard_sd: bool = False
# Dictionary defining the iterations and desired thresholds to apply iterative latent refinement in
thresholds: Dict[int, float] = field(default_factory=lambda: {0: 0.05, 10: 0.5, 20: 0.8})
# Scale factor for updating the denoised latent z_t
scale_factor: int = 20
# Start and end values used for scaling the scale factor - decays linearly with the denoising timestep
scale_range: tuple = field(default_factory=lambda: (1.0, 0.5))
# Whether to apply the Gaussian smoothing before computing the maximum attention value for each subject token
smooth_attentions: bool = True
# Standard deviation for the Gaussian smoothing
sigma: float = 0.5
# Kernel size for the Gaussian smoothing
kernel_size: int = 3
# Whether to save cross attention maps for the final results
save_cross_attention_maps: bool = False
def __post_init__(self):
self.output_path.mkdir(exist_ok=True, parents=True)
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