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DeutschFile:Linear diffusion noise scheduler.svg
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Summary
| Description | English: Illustration for a linear diffusion noise schedule. With settings . Matplotlib codeimport numpy as np import matplotlib.pyplot as plt class Scheduler: def __init__(self, beta_start=1e-4, beta_end=2e-2, num_train_timesteps=1000): self.beta_start = beta_start self.beta_end = beta_end self.num_train_timesteps = num_train_timesteps self.betas = np.linspace(self.beta_start, self.beta_end, self.num_train_timesteps) self.alphas = 1.0 - self.betas self.alphas_cumprod = np.cumprod(self.alphas, axis=0) sch = Scheduler(beta_start=1e-4, beta_end=2e-2, num_train_timesteps=1000) plt.plot([sch.alphas[t] for t in range(sch.num_train_timesteps)], label=r"$\alpha_t = 1 - \beta_t$") plt.plot([sch.alphas_cumprod[t] for t in range(sch.num_train_timesteps)], label=r"$\bar\alpha_t$") plt.plot([np.sqrt(1 - sch.alphas_cumprod[t]) for t in range(sch.num_train_timesteps)], label=r"$\sigma_t = \sqrt[[:Template:1 - \bar\alpha t]]$") plt.plot([sch.betas[t] for t in range(sch.num_train_timesteps)], label=r"$\beta_t$") plt.legend() plt.yscale('log') plt.xlabel(f'$t
) plt.savefig("diffusion_schedulers.svg") |
| Source | Own work  |
| Author |
|date=2024-09-07 |source=Own work |author=Cosmia Nebula }}
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 23:11, 7 September 2024 | | 614 × 461 (30 KB) | Cosmia Nebula | Uploaded while editing "Diffusion model" on en.wikipedia.org |
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