42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
import mlx.core as mx
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import numpy as np
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import logging
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log = logging.getLogger(__name__)
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class Config:
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precision: mx.Dtype = mx.bfloat16
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num_train_steps = 1000
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def __init__(
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self,
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num_inference_steps: int = 4,
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width: int = 1024,
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height: int = 1024,
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guidance: float = 4.0,
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):
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if width % 16 != 0 or height % 16 != 0:
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log.warning("Width and height should be multiples of 16. Rounding down.")
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self.width = 16 * (height // 16)
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self.height = 16 * (width // 16)
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base_sigmas = Config.base_sigmas(num_inference_steps)
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self.num_inference_steps = num_inference_steps
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self.guidance = guidance
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self.sigmas = mx.concatenate([base_sigmas, mx.zeros(1)])
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@staticmethod
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def base_sigmas(num_inference_steps):
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sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
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sigmas = mx.array(sigmas).astype(mx.float32)
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return sigmas
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def shift_sigmas(self):
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y1 = 0.5
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x1 = 256
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m = (1.15 - y1) / (4096 - x1)
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b = y1 - m * x1
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mu = m * self.width * self.height / 256 + b
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self.sigmas = mx.exp(mu) / (mx.exp(mu) + (1 / self.sigmas - 1))
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self.sigmas[-1] = 0
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