update defaults

sgm/inference/api.py

@@ -62,9 +62,9 @@ class SamplingParams:
  discretization: Discretization = Discretization.LEGACY_DDPM
  guider: Guider = Guider.VANILLA
  thresholder: Thresholder = Thresholder.NONE
- scale: float = 6.0
- aesthetic_score: float = 5.0
- negative_aesthetic_score: float = 5.0
+ scale: float = 5.0
+ aesthetic_score: float = 6.0
+ negative_aesthetic_score: float = 2.5
  img2img_strength: float = 1.0
  orig_width: int = 1024
  orig_height: int = 1024
@@ -181,20 +181,30 @@ def __init__(
  model_path = pathlib.Path(__file__).parent.parent.resolve() / "checkpoints"
  if not os.path.exists(model_path):
  # This supports development installs where checkpoints is root level of the repo
- model_path = pathlib.Path(__file__).parent.parent.parent.resolve() / "checkpoints"
+ model_path = (
+ pathlib.Path(__file__).parent.parent.parent.resolve()
+ / "checkpoints"
+ )
  if config_path is None:
- config_path = pathlib.Path(__file__).parent.parent.resolve() / "configs/inference"
+ config_path = (
+ pathlib.Path(__file__).parent.parent.resolve() / "configs/inference"
+ )
  if not os.path.exists(config_path):
  # This supports development installs where configs is root level of the repo
  config_path = (
- pathlib.Path(__file__).parent.parent.parent.resolve() / "configs/inference"
+ pathlib.Path(__file__).parent.parent.parent.resolve()
+ / "configs/inference"
  )
  self.config = str(pathlib.Path(config_path) / self.specs.config)
  self.ckpt = str(pathlib.Path(model_path) / self.specs.ckpt)
  if not os.path.exists(self.config):
- raise ValueError(f"Config {self.config} not found, check model spec or config_path")
+ raise ValueError(
+ f"Config {self.config} not found, check model spec or config_path"
+ )
  if not os.path.exists(self.ckpt):
- raise ValueError(f"Checkpoint {self.ckpt} not found, check model spec or config_path")
+ raise ValueError(
+ f"Checkpoint {self.ckpt} not found, check model spec or config_path"
+ )
  self.device = device
  self.model = self._load_model(device=device, use_fp16=use_fp16)
 
@@ -290,7 +300,9 @@ def wrap_discretization(
  ):
  return discretization # Already wrapped
  if image_strength is not None and image_strength < 1.0 and image_strength > 0.0:
- discretization = Img2ImgDiscretizationWrapper(discretization, strength=image_strength)
+ discretization = Img2ImgDiscretizationWrapper(
+ discretization, strength=image_strength
+ )
 
  if (
  noise_strength is not None
@@ -349,7 +361,9 @@ def refiner(
 
 def get_guider_config(params: SamplingParams):
  if params.guider == Guider.IDENTITY:
- guider_config = {"target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"}
+ guider_config = {
+ "target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"
+ }
  elif params.guider == Guider.VANILLA:
  scale = params.scale