98 lines
4.3 KiB
Python
98 lines
4.3 KiB
Python
import torch
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import ldm.models.diffusion.ddpm
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import ldm.models.diffusion.ddim
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import ldm.models.diffusion.plms
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from ldm.models.diffusion.ddim import noise_like
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from ldm.models.diffusion.sampling_util import norm_thresholding
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@torch.no_grad()
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def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
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temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
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unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, dynamic_threshold=None):
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b, *_, device = *x.shape, x.device
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def get_model_output(x, t):
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if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
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e_t = self.model.apply_model(x, t, c)
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else:
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x_in = torch.cat([x] * 2)
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t_in = torch.cat([t] * 2)
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if isinstance(c, dict):
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assert isinstance(unconditional_conditioning, dict)
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c_in = {}
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for k in c:
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if isinstance(c[k], list):
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c_in[k] = [
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torch.cat([unconditional_conditioning[k][i], c[k][i]])
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for i in range(len(c[k]))
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]
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else:
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c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
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else:
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c_in = torch.cat([unconditional_conditioning, c])
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e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
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e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
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if score_corrector is not None:
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assert self.model.parameterization == "eps"
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e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
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return e_t
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alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
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alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
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sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
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sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
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def get_x_prev_and_pred_x0(e_t, index):
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# select parameters corresponding to the currently considered timestep
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a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
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a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
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sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
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sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
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# current prediction for x_0
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pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
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if quantize_denoised:
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pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
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if dynamic_threshold is not None:
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pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
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# direction pointing to x_t
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dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
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noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
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if noise_dropout > 0.:
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noise = torch.nn.functional.dropout(noise, p=noise_dropout)
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x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
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return x_prev, pred_x0
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e_t = get_model_output(x, t)
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if len(old_eps) == 0:
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# Pseudo Improved Euler (2nd order)
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x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
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e_t_next = get_model_output(x_prev, t_next)
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e_t_prime = (e_t + e_t_next) / 2
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elif len(old_eps) == 1:
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# 2nd order Pseudo Linear Multistep (Adams-Bashforth)
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e_t_prime = (3 * e_t - old_eps[-1]) / 2
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elif len(old_eps) == 2:
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# 3nd order Pseudo Linear Multistep (Adams-Bashforth)
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e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
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elif len(old_eps) >= 3:
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# 4nd order Pseudo Linear Multistep (Adams-Bashforth)
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e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
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x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
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return x_prev, pred_x0, e_t
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def do_inpainting_hijack():
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# p_sample_plms is needed because PLMS can't work with dicts as conditionings
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ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
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