Upload config.py

config.py

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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)