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""" |
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https://github.com/SHI-Labs/Versatile-Diffusion |
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""" |
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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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import streamlit as st |
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from .diffusion_utils import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like |
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from .ddim import DDIMSampler |
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class DDIMSampler_VD(DDIMSampler): |
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@torch.no_grad() |
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def sample(self, |
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steps, |
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shape, |
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xt=None, |
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condition=None, |
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unconditional_guidance_scale=1., |
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xtype='image', |
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condition_types=['text'], |
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eta=0., |
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temperature=1., |
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mix_weight=None, |
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noise_dropout=0., |
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verbose=True, |
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log_every_t=100, |
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progress_bar=False, ): |
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self.make_schedule(ddim_num_steps=steps, ddim_eta=eta, verbose=verbose) |
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print(f'Data shape for DDIM sampling is {shape}, eta {eta}') |
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samples, intermediates = self.ddim_sampling( |
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shape, |
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xt=xt, |
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condition=condition, |
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unconditional_guidance_scale=unconditional_guidance_scale, |
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xtype=xtype, |
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condition_types=condition_types, |
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ddim_use_original_steps=False, |
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noise_dropout=noise_dropout, |
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temperature=temperature, |
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log_every_t=log_every_t, |
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mix_weight=mix_weight, |
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progress_bar=progress_bar, ) |
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return samples, intermediates |
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@torch.no_grad() |
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def ddim_sampling(self, |
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shape, |
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xt=None, |
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condition=None, |
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unconditional_guidance_scale=1., |
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xtype=['image'], |
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condition_types=['text'], |
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ddim_use_original_steps=False, |
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timesteps=None, |
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noise_dropout=0., |
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temperature=1., |
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mix_weight=None, |
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log_every_t=100, |
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progress_bar=False,): |
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device = self.model.device |
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dtype = condition[0][0].dtype |
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if isinstance(shape[0], list): |
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bs = shape[0][0] |
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else: |
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bs = shape[0] |
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if xt is None: |
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if isinstance(shape[0], list): |
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xt = [torch.randn(shape_i, device=device, dtype=dtype) for shape_i in shape] |
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else: |
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xt = torch.randn(shape, device=device, dtype=dtype) |
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if timesteps is None: |
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timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps |
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elif timesteps is not None and not ddim_use_original_steps: |
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subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 |
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timesteps = self.ddim_timesteps[:subset_end] |
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intermediates = {'pred_xt': [], 'pred_x0': []} |
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time_range = reversed(range(0, timesteps)) if ddim_use_original_steps else np.flip(timesteps) |
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total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] |
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pred_xt = xt |
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iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) |
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if progress_bar is not None: |
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progress_bar.progress(0) |
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progress_bar.text("Generating samples...") |
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for i, step in enumerate(iterator): |
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if progress_bar is not None: |
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progress_bar.progress(i/total_steps) |
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index = total_steps - i - 1 |
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ts = torch.full((bs,), step, device=device, dtype=torch.long) |
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outs = self.p_sample_ddim( |
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pred_xt, |
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condition, |
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ts, index, |
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unconditional_guidance_scale=unconditional_guidance_scale, |
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xtype=xtype, |
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condition_types=condition_types, |
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use_original_steps=ddim_use_original_steps, |
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noise_dropout=noise_dropout, |
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temperature=temperature, |
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mix_weight=mix_weight, ) |
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pred_xt, pred_x0 = outs |
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if index % log_every_t == 0 or index == total_steps - 1: |
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intermediates['pred_xt'].append(pred_xt) |
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intermediates['pred_x0'].append(pred_x0) |
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if progress_bar is not None: |
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progress_bar.success("Sampling complete.") |
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return pred_xt, intermediates |
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@torch.no_grad() |
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def p_sample_ddim(self, x, |
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condition, |
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t, index, |
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unconditional_guidance_scale=1., |
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xtype=['image'], |
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condition_types=['text'], |
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repeat_noise=False, |
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use_original_steps=False, |
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noise_dropout=0., |
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temperature=1., |
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mix_weight=None, ): |
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b, *_, device = *x[0].shape, x[0].device |
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x_in = [] |
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for x_i in x: |
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x_in.append(torch.cat([x_i] * 2)) |
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t_in = torch.cat([t] * 2) |
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out = self.model.model.diffusion_model( |
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x_in, t_in, condition, xtype=xtype, condition_types=condition_types, mix_weight=mix_weight) |
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e_t = [] |
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for out_i in out: |
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e_t_uncond_i, e_t_i = out_i.chunk(2) |
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e_t_i = e_t_uncond_i + unconditional_guidance_scale * (e_t_i - e_t_uncond_i) |
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e_t_i = e_t_i.to(device) |
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e_t.append(e_t_i) |
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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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x_prev = [] |
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pred_x0 = [] |
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device = x[0].device |
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dtype = x[0].dtype |
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for i, xtype_i in enumerate(xtype): |
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if xtype_i in ['image', 'frontal', 'lateral']: |
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extended_shape = (b, 1, 1, 1) |
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elif xtype_i == 'video': |
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extended_shape = (b, 1, 1, 1, 1) |
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elif xtype_i == 'text': |
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extended_shape = (b, 1) |
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elif xtype_i == 'audio': |
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extended_shape = (b, 1, 1, 1) |
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a_t = torch.full(extended_shape, alphas[index], device=device, dtype=dtype) |
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a_prev = torch.full(extended_shape, alphas_prev[index], device=device, dtype=dtype) |
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sigma_t = torch.full(extended_shape, sigmas[index], device=device, dtype=dtype) |
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sqrt_one_minus_at = torch.full(extended_shape, sqrt_one_minus_alphas[index], device=device, dtype=dtype) |
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pred_x0_i = (x[i] - sqrt_one_minus_at * e_t[i]) / a_t.sqrt() |
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dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t[i] |
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noise = sigma_t * noise_like(x[i], 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_i = a_prev.sqrt() * pred_x0_i + dir_xt + noise |
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x_prev.append(x_prev_i) |
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pred_x0.append(pred_x0_i) |
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return x_prev, pred_x0 |
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