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