mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
2b1aaf4ee7
- scripts and documentation updated to match - ran preflight checks on both web and CLI and seems to be working
136 lines
4.8 KiB
Python
136 lines
4.8 KiB
Python
"""SAMPLING ONLY."""
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import torch
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import numpy as np
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from tqdm import tqdm
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from functools import partial
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from ldm.invoke.devices import choose_torch_device
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from ldm.models.diffusion.sampler import Sampler
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from ldm.modules.diffusionmodules.util import noise_like
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class PLMSSampler(Sampler):
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def __init__(self, model, schedule='linear', device=None, **kwargs):
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super().__init__(model,schedule,model.num_timesteps, device)
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# this is the essential routine
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@torch.no_grad()
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def p_sample(
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self,
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x, # image, called 'img' elsewhere
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c, # conditioning, called 'cond' elsewhere
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t, # timesteps, called 'ts' elsewhere
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index,
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repeat_noise=False,
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use_original_steps=False,
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quantize_denoised=False,
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temperature=1.0,
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noise_dropout=0.0,
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score_corrector=None,
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corrector_kwargs=None,
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unconditional_guidance_scale=1.0,
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unconditional_conditioning=None,
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old_eps=[],
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t_next=None,
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**kwargs,
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):
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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 (
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unconditional_conditioning is None
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or unconditional_guidance_scale == 1.0
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):
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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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c_in = torch.cat([unconditional_conditioning, c])
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e_t_uncond, e_t = self.model.apply_model(
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x_in, t_in, c_in
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).chunk(2)
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e_t = e_t_uncond + unconditional_guidance_scale * (
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e_t - e_t_uncond
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)
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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(
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self.model, e_t, x, t, c, **corrector_kwargs
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)
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return e_t
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alphas = (
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self.model.alphas_cumprod
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if use_original_steps
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else self.ddim_alphas
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)
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alphas_prev = (
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self.model.alphas_cumprod_prev
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if use_original_steps
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else self.ddim_alphas_prev
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)
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sqrt_one_minus_alphas = (
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self.model.sqrt_one_minus_alphas_cumprod
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if use_original_steps
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else self.ddim_sqrt_one_minus_alphas
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)
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sigmas = (
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self.model.ddim_sigmas_for_original_num_steps
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if use_original_steps
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else self.ddim_sigmas
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)
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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(
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(b, 1, 1, 1), alphas_prev[index], device=device
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)
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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(
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(b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
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)
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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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# direction pointing to x_t
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dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
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noise = (
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sigma_t
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* noise_like(x.shape, device, repeat_noise)
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* temperature
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)
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if noise_dropout > 0.0:
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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 = (
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55 * e_t
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- 59 * old_eps[-1]
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+ 37 * old_eps[-2]
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- 9 * old_eps[-3]
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) / 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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