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Add sampling_settings so models can specify specific sampling settings.
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@ -24,7 +24,7 @@ class ModelSamplingDiscrete(torch.nn.Module):
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super().__init__()
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super().__init__()
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beta_schedule = "linear"
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beta_schedule = "linear"
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if model_config is not None:
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if model_config is not None:
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beta_schedule = model_config.beta_schedule
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beta_schedule = model_config.sampling_settings.get("beta_schedule", beta_schedule)
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self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3)
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self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3)
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self.sigma_data = 1.0
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self.sigma_data = 1.0
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@ -19,7 +19,7 @@ class BASE:
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clip_prefix = []
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clip_prefix = []
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clip_vision_prefix = None
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clip_vision_prefix = None
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noise_aug_config = None
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noise_aug_config = None
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beta_schedule = "linear"
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sampling_settings = {}
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latent_format = latent_formats.LatentFormat
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latent_format = latent_formats.LatentFormat
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@classmethod
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@classmethod
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