| from typing import List, Tuple |
| from scipy import interpolate |
| import numpy as np |
| import torch |
| import matplotlib.pyplot as plt |
| from IPython.display import clear_output |
| import abc |
|
|
|
|
| class GuideModel(torch.nn.Module, abc.ABC): |
| def __init__(self) -> None: |
| super().__init__() |
|
|
| @abc.abstractmethod |
| def preprocess(self, x_img): |
| pass |
|
|
| @abc.abstractmethod |
| def compute_loss(self, inp): |
| pass |
|
|
|
|
| class Guider(torch.nn.Module): |
| def __init__(self, sampler, guide_model, scale=1.0, verbose=False): |
| """Apply classifier guidance |
| |
| Specify a guidance scale as either a scalar |
| Or a schedule as a list of tuples t = 0->1 and scale, e.g. |
| [(0, 10), (0.5, 20), (1, 50)] |
| """ |
| super().__init__() |
| self.sampler = sampler |
| self.index = 0 |
| self.show = verbose |
| self.guide_model = guide_model |
| self.history = [] |
|
|
| if isinstance(scale, (Tuple, List)): |
| times = np.array([x[0] for x in scale]) |
| values = np.array([x[1] for x in scale]) |
| self.scale_schedule = {"times": times, "values": values} |
| else: |
| self.scale_schedule = float(scale) |
|
|
| self.ddim_timesteps = sampler.ddim_timesteps |
| self.ddpm_num_timesteps = sampler.ddpm_num_timesteps |
|
|
|
|
| def get_scales(self): |
| if isinstance(self.scale_schedule, float): |
| return len(self.ddim_timesteps)*[self.scale_schedule] |
|
|
| interpolater = interpolate.interp1d(self.scale_schedule["times"], self.scale_schedule["values"]) |
| fractional_steps = np.array(self.ddim_timesteps)/self.ddpm_num_timesteps |
| return interpolater(fractional_steps) |
|
|
| def modify_score(self, model, e_t, x, t, c): |
|
|
| |
| scale = self.get_scales()[self.index] |
|
|
| if (scale == 0): |
| return e_t |
|
|
| sqrt_1ma = self.sampler.ddim_sqrt_one_minus_alphas[self.index].to(x.device) |
| with torch.enable_grad(): |
| x_in = x.detach().requires_grad_(True) |
| pred_x0 = model.predict_start_from_noise(x_in, t=t, noise=e_t) |
| x_img = model.first_stage_model.decode((1/0.18215)*pred_x0) |
|
|
| inp = self.guide_model.preprocess(x_img) |
| loss = self.guide_model.compute_loss(inp) |
| grads = torch.autograd.grad(loss.sum(), x_in)[0] |
| correction = grads * scale |
|
|
| if self.show: |
| clear_output(wait=True) |
| print(loss.item(), scale, correction.abs().max().item(), e_t.abs().max().item()) |
| self.history.append([loss.item(), scale, correction.min().item(), correction.max().item()]) |
| plt.imshow((inp[0].detach().permute(1,2,0).clamp(-1,1).cpu()+1)/2) |
| plt.axis('off') |
| plt.show() |
| plt.imshow(correction[0][0].detach().cpu()) |
| plt.axis('off') |
| plt.show() |
|
|
|
|
| e_t_mod = e_t - sqrt_1ma*correction |
| if self.show: |
| fig, axs = plt.subplots(1, 3) |
| axs[0].imshow(e_t[0][0].detach().cpu(), vmin=-2, vmax=+2) |
| axs[1].imshow(e_t_mod[0][0].detach().cpu(), vmin=-2, vmax=+2) |
| axs[2].imshow(correction[0][0].detach().cpu(), vmin=-2, vmax=+2) |
| plt.show() |
| self.index += 1 |
| return e_t_mod |
